Group Link: https://chat.whatsapp.com/K54tAk9YyFPKJWAJQt7iTK

For the London group, this link didn't work for me.

I am excited about improvements to the wiki. Might write some. 

Claims

 The claims logo is ugly. 

This piece was inspired partly by @KatjaGrace who has a short story idea that I hope to cowrite with her. Also partly inspired by @gwern's discussion with @dwarkeshsp 

What would you conclude or do if

It's hard to know, because I feel this thing. I hope I might be tempted to follow the breadcrumbs suggested and see that humans really do talk about consciousness a lot. Perhaps to try and build a biological brain and quiz it. 

I was not at the session. Yes Claude did write it. I assume the session was run by Daniel Kokatajlo or Eli Lifland. 

If I had to guess, I would guess that the prompt show is all it got. (65%)

I wish we kept and upvotable list of journalists so we could track who is trusted in the community and who isn't.

Seems not hard. Just a page with all the names as comments. I don't particularly want to add people, so make the top level posts anonymous. Then anyone can add names and everyone else can vote if they are trustworthy and add comments of experiences with them.

This journalist wants to talk to me about the Zizian stuff.

https://www.businessinsider.com/author/rob-price 

I know about as much as the median rat, but I generally think it's good to answer journalists on substantive questions.

Do you think is a particularly good or bad idea, do you have any comments about this particular journalist. Feel free to DM me.

How might I combine these two datasets? One is a binary market, the other is a date market. So for any date point, one is a percentage P(turing test before 2030) the other is a cdf across a range of dates P(weakly general AI publicly known before that date).

Here are the two datasets. 

Suggestions:

• Fit a normal distribution to the turing test market such that the 1% is at the current day and the P(X<2030) matches the probability for that data point
• Mirror the second data set but for each data point elevate the probabilities before 2030 such that P(X<2030) matches the probability for the first dataset

Thoughts:

Overall the problem is that one doesn't know what distribution to fit the second single datapoint to. The second suggestion just uses the distribution of the second data set for the first, but that seems quite complext.

"Why would you want to combine these datasets?"

Well they are two different views when something like AGI will appear. Seems good to combine them.

Suggested market. Happy to take suggestions on how to improve it:

https://manifold.markets/NathanpmYoung/will-o3-perform-as-well-on-the-fron?play=true