Donald Hobson

MMath Cambridge. Currently studying postgrad at Edinburgh.


Neural Networks, More than you wanted to Show
Logical Counterfactuals and Proposition graphs

Wiki Contributions


Epistemic status: Highly speculative hypothesis generation.

I had a similar, but slightly different picture. My picture was the tentacle covered blob.

When doing gradient descent from a random point, you usually get to a tentacle. (Ie the red trajectory) 

When the system has been running for a while, it is randomly sampling from the black region. Most of the volume is in the blob. (The randomly wandering particle can easily find its way from the tentacle to the blob, but the chance of it randomly finding the end of a tentacle from within the blob is small) 

Under this model, the generalization would work the same when using a valid sampling process. 

I think each little decision is throwing another few bits of info. A few bits for deciding how big the mantisa and exponent should be. A few bits for it being a 64 bit float. A few bits for subnormals. A few bits for inf and Nan.  A few bits for rounding errors. A bit for -0. And it all adds up. Not that we know how many bits the AI needs. If there is one standard computer architecture that all aliens use, then the AI can hack with very little info. If all alien computers have wildly different architectures, then floats carry a fair bit of info. 

Sure, binary is fairly natural, but there are a lot of details of IEEE floats that aren't.

Deterministic (but complicated) code can leak info about processor architecture. 

Firstly even simple integer overflow and floating point precision indicate being on a 32 or 64 bit based system.


Suppose you have a sum function. And this function sums a list by getting each of the 8 cores to add up 1/8th of the list, and then combining the results. The floating point roundoff errors reveal this internal structure. 

Add some other things like that, and it is now possible for your AI to guess your computer architecture. Is this enough to guess where a buffer overflow attack might work? Maybe. Maybe there aren't that many different processor architectures running code like it in the multiverse. 

What does the network do if you use SVD editing to knock out every uninterpretable column? What if you knock out everything interpretable? 

(If you can't see why a single modern society locking in their current values would be a tragedy of enormous proportions, imagine an ancient civilization such as the Romans locking in their specific morals 2000 years ago. Moral progress is real, and important.)

This really doesn't prove anything. That measurement shouldn't be taken by our values, but by the values of the ancient romans. 

Sure of course the morality of the past gets better and better. It's taking a random walk closer and closer to our morality. Now moral progress might be real. 

The place to look is inside our own value functions, if after 1000 years of careful philosophical debate, humanity decided it was a great idea to eat babies, would you say, "well if you have done all that thinking, clearly you are wiser than me". Or would you say "Arghh, no. Clearly something has broken in your philosophical debate"? That is a part of your own meta value function, the external world can't tell you what to think here (unless you have a meta meta value function. But then you have to choose that for yourself) 

It doesn't help that human values seem to be inarticulate half formed intuitions, and the things we call our values are often instrumental goals. 

If, had ASI not been created, humans would have gone extinct to bioweapons, and pandas would have evolved intelligence, it the extinction of humans and the rise of panda-centric morality just part of moral progress? 

If aliens arrive, and offer to share their best philosophy with us, is the alien influence part of moral progress, or an external fact to be removed? 

If advertisers basically learn to brainwash people to sell more product, is that part of moral progress?

Suppose, had you not made the AI, that Joe Bloggs would have made an AI 10 years later. Joe Bloggs would actually have succeeded at alignment. And would have imposed his personal whims on all humanity forever. If you are trying not to unduely influence the future, do you make everyone beholden to the whims of Joe, as they would be without your influence. 

My personal CEV cares about fairness, human potential, moral progress, and humanity’s ability to choose its own future, rather than having a future imposed on them by a dictator. I'd guess that the difference between "we run CEV on Nate personally" and "we run CEV on humanity writ large" is nothing (e.g., because Nate-CEV decides to run humanity's CEV), and if it's not nothing then it's probably minor.

Wait. The whole point of the CEV is to get the AI to extrapolate what you would want if you were smarter and more informed. That is, the delta from your existing goals to your CEV should be unknowable to you, because if you know your destination you are already there. This sounds like your object level values. And they sound good, as judged by your (and my) object level values.


I mean there is a sense in which I agree that locking in say your favourite political party, or a particular view on abortion, is stupid. Well I am not sure that particular view on abortion would be actually bad, it would probably have near no effect in a society of posthuman digital minds. These are things that are fairly clearly instrumental. If I learned that after careful philosophical consideration, and analysis of lots of developmental neurology data, people decided abortion was really bad, I would take that seriously. They have probably realized a moral truth I do not know. 

I think I have a current idea of what is right, with uncertainty bars. When philosophers come to an unexpected conclusion, it is some evidence that the conclusion is right, and also some evidence the philosopher has gone mad. 

My best guess bio anchors adaption suggests a median estimate for the availability of compute to train TAI

My best guess is in the past. I think GPT3 levels of compute and data are sufficient, with the right algorithm, to make a superhuman AI. 

This current version is dumb, but still exerts some optimization pressure. (Just the bits of optimization out are at most the bits of selection put into its utility function.) 

I have strong doubts this is actually any help. Ok, maybe the AI comes up with new simpler math. It's still new math that will take the humans a while to get used to. There will probably be lots of options to add performance and complexity. So either we are banning the extra complexity, using our advanced understanding, and just using the simpler math at the cost of performance, or the AI soon gets complex again before humans have understood the simple math. 

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