Trying to learn
Phib
Karma: 52
Honestly, maybe further controversial opinion, but this [30 million for a board seat at what would become the lead co. for AGI, with a novel structure for nonprofit control that could work?] still doesn’t feel like necessarily as bad a decision now as others are making it out to be?
The thing that killed all value of this deal was losing the board seat(s?), and I at least haven’t seen much discussion of this as a mistake.
I’m just surprised so little prioritization was given to keeping this board seat, it was probably one of the most important assets of the “AI safety community and allies”, and there didn’t seem to be any real fight with Sam Altman’s camp for it.
So Holden has the board seat, but has to leave because of COI, and endorses Toner to replace, ”… Karnofsky cited a potential conflict of interest because his wife, Daniela Amodei, a former OpenAI employee, helped to launch the AI company Anthropic.
Given that Toner previously worked as a senior research analyst at Open Philanthropy, Loeber speculates that Karnofsky might’ve endorsed her as his replacement.”
Like, maybe it was doomed if they only had one board seat (Open Phil) vs whoever else is on the board, and there’s a lot of shuffling about as Musk and Hoffman also leave for COIs, but start of 2023 it seems like there is an “AI Safety” half to the board, and a year later there are now none. Maybe it was further doomed if Sam Altman has the, take the whole company elsewhere, card, but idk… was this really inevitable? Was there really not a better way to, idk, maintain some degree of control and supervision of this vital board over the years since OP gave the grant?
“they serendipitously chose guinea pigs, the one animal besides human beings and monkeys that requires vitamin C in its diet.“
This recent post I think describes this same phenomena but not from the same level of ‘necessity’ as, say, cures to big problems. Kinda funny too: https://www.lesswrong.com/posts/oA23zoEjPnzqfHiCt/there-is-way-too-much-serendipity.
So here was my initial quick test, I haven’t spent much time on this either, but have seen the same images of faces on subreddits etc. and been v impressed. I think asking for emotions was a harder challenge vs just making a believable face/hand, oops
I really appreciate your descriptions of the distinctive features of faces and of pareidolia, and do agree that faces are more often better represented than hands, specifically hands often have the more significant/notable issues (misshapen/missing/overlapped fingers). Versus with faces where there’s nothing as significant as missing an eye, but it can be hard to portray something more specific like an emotion (though same can be said for, e.g. getting Dalle not to flip me off when I ask for an index finger haha).
Rather difficult to label or prompt a specific hand orientation you’d like as well, versus I suppose, an emotion (a lot more descriptive words for the orientation of a face than a hand)
So yeah, faces do work, and regardless of my thoughts on uncanny valley of some faces+emotions, I actually do think hands (OP subject) are mostly a geometric complexity thing, maybe we see our own hands so much that we are more sensitive to error? But they don’t have the same meaning to them as faces for me (minute differences for slightly different emotions, and benefitting perhaps from being able to accurately tell).
I think if this were true, then it would also hold that faces are done rather poorly right now which, maybe? Doing some quick tests, yeah, both faces and hands at least on Dalle-3 seem to be similar levels of off to me.
Wow, I’m impressed it caught itself, was just trying to play with that 3 x 3 problem too. Thanks!
I don’t know [if I understand] full rules so don’t know if this satisfies, but here:
https://chat.openai.com/share/0089e226-fe86-4442-ba07-96c19ac90bd2
Kinda commenting on stuff like “Please don’t throw your mind away” or any advice not to fully defer judgment to others (and not intending to just straw man these! They’re nuanced and valuable, just meaning to next step it).
In my circumstance and I imagine many others who are young and trying to learn and trying to get a job, I think you have to defer to your seniors/superiors/program to a great extent, or at least to the extent where you accept or act on things (perform research, support ops) that you’re quite uncertain about.
Idk there’s a lot more nuance here to this conversation as with any, of course. Maybe nobody is certain of anything and they’re just staking a claim so that they can be proven right or wrong and experiment in this way, producing value in their overconfidence. But I do get a sense of young/new people coming into a field that is even slightly established, requiring to some extent to defer to others for their own sake.
I don’t mean to present myself as the “best arguments that could be answered here” or at all representative of the alignment community. But just wanted to engage. I appreciate your thoughts!
Well, one argument for potential doom doesn’t necessitate an adversarial AI, but rather people using increasingly powerful tools in dumb and harmful ways (in the same class of consideration for me as nuclear weapons; my dumb imagined situation of this is a government using AI to continually scale up surveillance and maybe we eventually get to a position like in 1984)
Another point is that a sufficiently intelligent and agentic AI would not need humans, it would probably eventually be suboptimal to rely on humans for anything. And it kinda feels to me like this is what we are heavily incentivized to design, the next best and most capable system. In terms of efficiency, we want to get rid of the human in the loop, that person’s expensive!
Idk the public access of some of these things, like with nonlinear’s recent round, but seeing a lot of apps there and organized by category, reminded me of this post a little bit.
edit—in terms of seeing what people are trying to do in the space. Though I imagine this does not capture the biggest players that do have funding.
btw small note that I think accumulations of grant applications are probably pretty good sources of info.
BTW—this video is quite fun. Seems relevant re: Paperclip Maximizer and nanobots.
low commit here but I’ve previously used nanotech as an example (rather than a probable outcome) of a class somewhat known unknowns—to portray possible future risks that we can imagine as possible while not being fully conceived. So while grey goo might be unlikely, it seems that precursor to grey goo of a pretty intelligent system trying to mess us up is the thing to be focused on, and this is one of its many possibilities that we can even imagine
I rather liked this post (and I’ll put it on both EAF and LW versions)
https://www.lesswrong.com/posts/PQtEqmyqHWDa2vf5H/a-quick-guide-to-confronting-doom
Particularly the comment by Jakob Kraus reminded me that many people have faced imminent doom (not of human species, but certainly quite terrible experiences).
Hi, writing this while on the go but just throwing it out there, this seems to be Sam Altman’s intent with OpenAI in pursuing fast timelines with slow takeoffs.
I am unaware of those decisions at the time. I imagine people are some degree of ‘making decisions under uncertainty’, even if that uncertainty could be resolved by info somewhere out there. Perhaps there’s some optimization of how much time you spend looking into something and how right you could expect to be?
Anecdote of me (not super rationalist-practiced, also just at times dumb) - I sometimes discover stuff I briefly took to be true in passing to be false later. Feels like there’s an edge of truth/falsehoods that we investigate pretty loosely but still use a heuristic of some valence of true/false maybe a bit too liberally at times.
LLMs as a new benchmark for human labor. Using ChatGPT as a control group versus my own efforts to see if my efforts are worth more than the (new) default
Thanks for writing this, enjoyed it. I was wondering how to best represent this to other people, perhaps with an example of 5 and 10 where you let a participant make the mistake, and then question their reasoning etc. lead them down the path laid out in your post of rationalization after the decision before finally you show them their full thought process in post. I could certainly imagine myself doing this and I hope I’d be able to escape my faulty reasoning…
Reminds me of this trend: https://mashable.com/article/chatgpt-make-it-more In which people ask dalle to make images generated more whatever quality. More swiss, bigger water bottle, and eventually you get ‘spirituality’ or meta as the model tries its best to take a step up each time.
Also, I feel like the context being added to the prompt, as you go on in the context window and it takes some previous details from your conversation, is warbled and further prompts warbling.