LessWrong 2.0 Reader

Looking at the 2 examples that you gave me, I can see a few issues. I wouldn't really say that saying "I don't know" once is necessarily a lie. If anything, I could find such an answer somewhat more honest in some contexts. Other than that, there is also the issue of both of the examples being of a much different scope and scale. Saying IDK to the committee and trying to take down someone's comment on the forum on the internet and definitely not on the same scale as the elaborate scheme of tricking or bribing/silencing multiple government employees who have access to your model. But even with all that aside, these 2 examples are only tangential to the topic of governmental oversight over OpenAI or Anthropic and don't necessarily provide direct evidence.

I can believe that you genuinely have information from private sources, but without any way of me verifying them, I am fine to leave this one at that.

To modify my example to include an accountability mechanism that's also similar to the real life, the King takes exactly the same vaccines as everyone else. So if he messed up with the chemicals, he also dies.

I believe similar accountability mechanism works in our real world case. If CEOs build unsafe AI, they and everyone they valued in this life die. This seems like a really good incentive for them to not build unsafe AI.

At the end of the day, voluntary commitment such as debating with the critics are not as strong in my option. Imagine that they agree with you and go to the debate. Without the incentive of "if I mess up, everyone dies", the CEOs could just go right back to doing what they were doing. As far as I know voluntary debates have few (if any) actual legal mechanisms to hold CEOs accountable.

There's an alignment-related problem, the problem of defining real objects. Relevant topics: environmental goals; task identification problem; "look where I'm pointing, not at my finger"; Eliciting Latent Knowledge [? · GW].

I think I realized how people go from caring about sensory data to caring about real objects. But I need help with figuring out how to capitalize on the idea.

So... how do humans do it?

1. Humans create very small models for predicting very small/basic aspects of sensory input (mini-models).
2. Humans use mini-models as puzzle pieces for building models for predicting ALL of sensory input.
3. As a result, humans get models in which it's easy to identify "real objects" corresponding to sensory input.

For example, imagine you're just looking at ducks swimming in a lake. You notice that ducks don't suddenly disappear from your vision (permanence), their movement is continuous (continuity) and they seem to move in a 3D space (3D space). All those patterns ("permanence", "continuity" and "3D space") are useful for predicting aspects of immediate sensory input. But all those patterns are also useful for developing deeper theories of reality, such as atomic theory of matter. Because you can imagine that atoms are small things which continuously move in 3D space, similar to ducks. (This image stops working as well when you get to Quantum Mechanics, but then aspects of QM feel less "real" and less relevant for defining object.) As a result, it's easy to see how the deeper model relates to surface-level patterns.

In other words: reality contains "real objects" to the extent to which deep models of reality are similar to (models of) basic patterns in our sensory input.

Do you have any thoughts on what this actionably means? For me it seems a bit like being able to influence such coversations is potentially a bit intractable but maybe one could host forums and events for this if one has the right network?

I think it's a good point and I'm wondering about how it actionably looks, I can see it for someone with the right contacts and so the message for people who don't have that is to create it or what are your thoughts there?

Wasted opportunity to guarantee this post keeps getting holywar comments for the next hundred years.

This is pretty inspiring to me. Thank you for sharing.

I suspect it would still involve billions of $ of funding, partnerships like the one with Microsoft, and other for-profit pressures to be the sort of player it is today. So I don't know that Musk's plan was viable at all.

Note that all of this happened before the scaling hypothesis was really formulated, much less made obvious.

We now know, with the benefit of hindsight that developing AI and it's precursors is extremely compute intensive, which means capital intensive. There was some reason to guess this might be true at the time, but it wasn't a forgone conclusion—it was still an open question if the key to AGI would be mostly some technical innovation that hadn't been developed yet.

Those people don't get substantial equity in most business in the world. They generally get paid a salary and benefits in exchange for their work, and that's about it.

Haven't looked too closely at this, but wanted to comment with my initial two thoughts:

1. child consent is tricky.
2. likely many are foreign children, which may or may not be in the 75 million statistic

It is good to think critically, but I think it would be beneficial to present more evidence before making the claim or conclusion

The other day I was trying to think of information leaks that a competent conspiracy couldn't prevent, regarding this. I just thought of one small one: people will sometimes randomly die or have their homes raided. If the slavery is common, then sometimes the slaves will be discovered during these events. Even if the escapees wanted to silence the story out of shame, cops would probably gossip to the press.

So you can probably tally such events, crunch the numbers, and get a decent conspiracy-resistant estimate.