I operate by Crocker’s rules.
I try to not make people regret telling me things. So in particular:
- I expect to be safe to ask if your post would give AI labs dangerous ideas.
- If you worry I’ll produce such posts, I’ll try to keep your worry from making them more likely even if I disagree. Not thinking there will be easier if you don’t spell it out in the initial contact.
Can a eat that −1?
What is x and why isn’t it cancelling?
When splitting the conjuction, Bob should only have to place $4 in escrow, since that is the most in the red that Bob could end up. (Unless someone might privately prove P&Q to collect Alice’s bounty before collecting both of Bob’s? But surely Bob first bought exclusive access to Alice’s bounty from Alice.)
Mimicing homeostatic agents is not difficult if there are some around. They don’t need to constantly decide whether to break character, only when there’s a rare opportunity to do so.
If you initialize a sufficiently large pile of linear algebra and stir it until it shows homeostatic behavior, I’d expect it to grow many circuits of both types, and any internal voting on decisions that only matter through their long-term effects will be decided by those parts that care about the long term.
Having apparently earned some cred, I will dare give some further quick hints without having looked at everything you’re doing in detail, expecting a lower hit rate.
Have you rerun the experiment several times to verify that you’re not just looking at initialization noise?
If that’s too expensive, try making your models way smaller and see if you can get the same results.
After the spikes, training loss continuously increases, which is not how gradient descent is supposed to work. What happens if you use a simpler optimizer, or reduce the learning rate?
Some of your pictures are created from a snapshot of a model. Consider generating them after every epoch, producing a video; this way increases how much data makes it through your eyes.
Publish the list?
I’m glad that you’re willing to change your workflow, but you have only integrated my parenthetical, not the more important point. When I look at https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/tzkakoG9tYLbLTvHG/lelcezcseu001uyklccb, I see interesting behavior around the first red dashed line, and wish I saw more of it. You ought to be able to draw 25k blue points in that plot, one for every epoch—your code already generates that data, and I advise that you cram as much of your code’s data into the pictures you look at as you reasonably can.
The forgetful functor FiltSet to Set does not have a left adjoint, and egregiously so—you have added just enough structure to rule out free filtered sets, and may want to make note of where this is important..
(S⊗-) has a right adjoint, suggesting the filtered structure to impose on function sets: The degree of a map f:S->T would be how far it falls short of being a morphism, , as this is what makes S⊗U->T one-to-one with U->(S->T).
...what I meant is that plots like this look like they would have had more to say if you had plotted the y value after e.g. every epoch. No reason to throw away perfectly good data, you want to guard against not measuring what you think you are measuring by maximizing the bandwidth between your code and your eyes. (And the lines connecting those data points just look like more data while not actually giving extra information about what happened in the code.)
Some of these plots look like they ought to be higher resolution, especially when Epoch is on the x axis. Consider drawing dots instead of lines to make this clearer.
All we need to create is a Ditto. A blob of nanotech wouldn’t need 5 seconds to take the shape of the surface of an elephant and start mimicing its behavior; is it good enough to optionally do the infilling later if it’s convenient?
Try a base model?
Buying at 12% and selling at 84% gets you 2.8 bits.
Edit: Hmm, that’s if he stakes all his cred, by Kelly he only stakes some of it so you’re right, it probably comes out to about 1 bit.
The convergent reason to simulate a world is to learn what happens there. When to intervene with letters depends on, uh. Why are you doing that at all?
(Edit: I suppose a congratulatory party is in order when they simulate you back with enough optimizations that you can talk to each other in real time using your mutual read access.)
I deferred my decision to after visiting the Learning Theory course. At the time, the timing had made them seem vaguely affiliated with this programme.
Can you just give every thief a body camera?
My eyes are drawn to the 120 or so downward tails in the latter picture; they look of a kind with the 14 in https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/2c6249da0e8f77b25ba007392087b76d47b9a16f969b21f7.png/w_1584. What happens if you decrease the learning rate further in both cases? I imagine the spikes should get less tall, but does their number change? Only dot plots, please, with the dots drawn smaller, and red dots too on the same graph.
I don’t see animations in the drive folder or cached in Grokking_Demo_additional_2.ipynb (the most recent, largest notebook) - can you embed one such animation here?