I liked it precisely because it threw theory out the window and showed that cheap talk is not a real commitment.

• Tarkin > I believe in CDT and I precommit to bla bla bla
• Leia > I belive in FDT and I totally precommit to bla bla bla
• Vader > Death Star goes brrrrr...

For me the main thing in this story was that cheap talk =/= real commitment. You can talk all you want about how "totally precommitted" you are, but this lacks some concreteness.

Also, I saw Vader as much less galaxy brained as you portray him. Destroying Alderaan at the end looked to me more like mad ruthlessness than calculated strategy. (And if Leia had known Vader's actual policy, she would have no incentive to confess.) Maybe one thing that Vader did achieve, is signal for the future that he really does not care and will be ruthless (but also signaled that it doesn't matter if you give in to him, which is dumb).

Anyway, I liked the story, but for the action, not for some deep theoretic insight.

Not sure if that's what happened in that example, but you can bet that a price will rise above some threshold, or fall below some threshold, using options. You can even do both at the same time, essentially betting that the price won't stay as it is now.

But whether you will make money that way depends on the price of options.

I wanna link to my favorite one: consciousness vs replicators. It doesn't really fit into this grid, but I think it really is the ultimate conflict.

(You can definitely skip the first 14 min of this video, as it's just ranking people's stages of development. Maybe even first 33 min if you wanna go straight to the point.)

I wonder what would happen if we run the simple version of that algorithm on LW comments. So that votes would have "polarity", and so each comment would have two vote-counts, let's say orange count and blue count. (Of course that would be only optionally enabled.)

Then we could sort the comments by the minimum of these counts, descending.

(I think it makes more sense to train it per post than globally. But then it would be useful only on very popular posts with lots of comments.)

Thanks, that's terrifying.

I hope we invent mindmelding before we invent all this. Maybe if people can feel those states themselves, they won't let the worst of them happen.

Unfortunately I didn't have any particular tasks in mind when I wrote it. I was vaguely thinking about settings as in:

• https://arxiv.org/pdf/2305.04388.pdf
• https://arxiv.org/pdf/2307.13702.pdf

Now that I though about it, for this particular transformers vs mamba experiment, I'd go with something even simpler. I want a task that is very easy sequentially, but hard to answer immediately. So for example a task like:

x = 5
x += 2
x *= 3
x **= 2
x -= 3
...

and then have a CoT:

after x = 5
5
after x += 2
7
...

And then we intervene on CoT to introduce some error in one operation, but still ask of the model to give the correct answer at the end. (Despite all steps after the error being irrelevant.) We can go even further and train the models to give the correct answer after inadequate CoT. And have a curriculum where at first it only needs to do one hidden operation, later two, and so on.

(It's an unrealistic setting, but the point is rather to check if the model is able at all to learn hidden sequential reasoning.)

Now, my hypothesis is that transformers will have some limited sequence length for which they can do it (probably smaller than their number of layers), but mamba won't have a limit.

I was working on this for six months

Can you say what you tried in these six months and how did it go?

Yeah, true. But it's also easier to do early, when no one is that invested in the hidden-recurrence architectures, and so there's less resistance, it doesn't break anyone's plans.

Maybe a strong experiment would be to compare mamba-3b and some SOTA 3b transformer, trained similarly, on several tasks where we can evaluate CoT faithfulness. (Although maybe at 3b capability level we won't see clear differences yet.) The hard part would be finding the right tasks.

the natural language bottleneck is itself a temporary stage in the evolution of AI capabilities. It is unlikely to be an optimal mind design; already many people are working on architectures that don't have a natural language bottleneck

This one looks fatal. (I think the rest of the reasons could be dealt with somehow.)

What existing alternative architectures do you have in mind? I guess mamba would be one?

Do you think it's realistic to regulate this? F.e. requiring that above certain size, models can't have recurrence that uses a hidden state, but recurrence that uses natural language (or images) is fine. (Or maybe some softer version of this, if alignment tax proves too high.)