Recently, I made RatSearch for googling within EA-adjecent webs. Now, you can try theGPT bot version! (GPT plus required) The bot is instructed to interpret what you want to know in relation to EA and search for it on the Forums. If it fails, it searches through the whole web, while prioritizing the orgs listed by EA News.
Cons: ChatGPT uses Bing, which isn’t entirely reliable when it comes to indexing less visited webs.
Pros: It’s fun for brainstorming EA connections/perspective, even when you just type a raw phrase like “public transport” or “particle physics”
Neutral: I have yet to experiment whether it works better when you explicitlylimit the search using the site: operator—try AltruSearch 2. It seems better at digging deeper within the EA ecosystem; AltruSearch 1 seems better at digging wider.
Update (12/8):The link now redirects to an updated version with very different instructions. You can still access the older version here.
Cool. I’d be interested in tentatively providing this search for free on EA News via the OpenAI API, depending on monthly costs. Do you know how to implement it?
I got access to Bing Chat. It seems: - It only searches through archived versions of websites (it doesn’t retrieve today’s news articles, it accessed an older version of my Wikipedia user site) - During archivation, it only downloads the content one can see without any engagement with the website (tested on Reddit “see spoiler” buttons which reveal new content in the code. It could retrieve info from posts that gained less attention but weren’t hidden behind the spoiler button) I. e. it’s still in a box of sorts, unless it’s much more intelligent than it pretends.
Edit: A recent ACX post argues text-predicting oracles might be safer, as their ability to form goals is super limited, but it provides 2 models how even they could be dangerous: By simulating an agent or via a human who decides to take bad advice like “run the paperclip maximizer code”. Scott implies thinking it would spontaneously form goals is extreme, linking a post by Veedrac. The best argument there seems to be: It only has memory equivalent to 10 human seconds. I find this convincing for the current models but it also seems limiting for the intelligence of these systems, so I’m afraid for future models, the incentives are aligned with reducing this safety valve.
Recently, I made RatSearch for googling within EA-adjecent webs. Now, you can try the GPT bot version! (GPT plus required)
The bot is instructed to interpret what you want to know in relation to EA and search for it on the Forums. If it fails, it searches through the whole web, while prioritizing the orgs listed by EA News.
Cons: ChatGPT uses Bing, which isn’t entirely reliable when it comes to indexing less visited webs.
Pros: It’s fun for brainstorming EA connections/perspective, even when you just type a raw phrase like “public transport” or “particle physics”
Neutral: I have yet to experiment whether it works better when you explicitly limit the search using the site: operator—try AltruSearch 2. It seems better at digging deeper within the EA ecosystem; AltruSearch 1 seems better at digging wider.
Update (12/8): The link now redirects to an updated version with very different instructions. You can still access the older version here.
Cool. I’d be interested in tentatively providing this search for free on EA News via the OpenAI API, depending on monthly costs. Do you know how to implement it?
Sorry, I don’t have any experience with that.
I got access to Bing Chat. It seems:
- It only searches through archived versions of websites (it doesn’t retrieve today’s news articles, it accessed an older version of my Wikipedia user site)
- During archivation, it only downloads the content one can see without any engagement with the website (tested on Reddit “see spoiler” buttons which reveal new content in the code. It could retrieve info from posts that gained less attention but weren’t hidden behind the spoiler button)
I. e. it’s still in a box of sorts, unless it’s much more intelligent than it pretends.
Edit: A recent ACX post argues text-predicting oracles might be safer, as their ability to form goals is super limited, but it provides 2 models how even they could be dangerous: By simulating an agent or via a human who decides to take bad advice like “run the paperclip maximizer code”. Scott implies thinking it would spontaneously form goals is extreme, linking a post by Veedrac. The best argument there seems to be: It only has memory equivalent to 10 human seconds. I find this convincing for the current models but it also seems limiting for the intelligence of these systems, so I’m afraid for future models, the incentives are aligned with reducing this safety valve.