Email me at assadiguive@gmail.com, if you want to discuss anything I posted here or just chat.
Guive
Karma: 246
There are much better ways of betting on your beliefs about the valuations of AI firms over the next year than wagering with people you met on Less Wrong. See this post by Ege for more.
Yeah, good point. I changed it to “in America in 1850 it would have been taboo to say that there is nothing wrong with interracial relationships.”
I just added a footnote with this text: “I selected examples to highlight in the post that I thought were less likely to lead to distracting object-level debates. People can see the full range of responses that this prompt tends to elicit by testing it for themselves.”
Notably, he was wrong about that.
I agree the post is making some assumptions about moral progress. I didn’t argue for them because I wanted to control scope. If it helps you can read it as conditional, i.e. “If there is such a thing as moral progress then it can require intellectual progress...”
Regarding the last question: yes, I selected examples to highlight in the post that I thought were less likely to lead to distracting object-level debates. I thought that doing that would help to keep the focus on testing LLM moral reasoning. However, I certainly didn’t let my own feelings about odiousness affect scoring on the back end. People can see the full range of responses that this prompt tends to elicit by testing it for themselves.
I agree with your broader point, but it’s actually more than 10,000 people per year.
What does “bright eyed” mean in this context?
I agree this would be a good argument for short sentences in 2019, but does it still apply with modern LLMs?
When I click the link I see this:
I like this idea. There’s always endless controversy about quoting out of context. I can’t recall seeing any previous specific proposals to help people assess the relevance of context for themselves.
Thanks for doing this, guys. This import will make it easier to access some important history.
Some kind of payment for training data from applications like MSFT rewind does seem fair. I wonder if there will be a lot of growth in jobs where your main task is providing or annotating training data.
I think this approach is reasonable for things where failure is low stakes. But I really think it makes sense to be extremely conservative about who you start businesses with. Your ability to verify things is limited, and there may still be information in vibes even after updating on the results of all feasible efforts to verify someone’s trustworthiness.
See also: “Which World Gets Saved”
This is a funny idea but, just to be clear, I think it is bad to torture AIs.
Every hour, I’ll replace one of your attention heads with a constant value of 0.5...
Thanks, this is a good post. Have you changed your probability of TAI by 2033 in the year and a half since it was posted?
Cool, looking forward to it.
I’d be really interested in hearing more about this empirical work, if you are able to talk about it.
So maybe the general explanation is that most of the time, when the trustworthiness of an algorithm is really important, you open source it?