In NZ we have biting bugs called sandflies which don't do this - you can often tell the moment they get you.
Yes, that's fair. I was ignoring scale but you're right that it's a better comparison if it is between a marginal new human and a marginal new AI.
Well, yes, the point of my post is just to point out that the number that actually matters is the end-to-end energy efficiency — and it is completely comparable to humans.
The per-flop efficiency is obviously worse. But, that's irrelevant if AI is already cheaper for a given task in real terms.
I admit the title is a little clickbaity but i am responding to a real argument (that humans are still "superior" to AI because the brain is more thermodynamically efficient per-flop)
I saw some numbers for algae being 1-2% efficient but it was for biomass rather than dietary energy. Even if you put the brain in the same organism, you wouldn't expect as good efficiency as that. The difference is that creating biomass (which is mostly long chains of glucose) is the first step, and then the brain must use the glucose, which is a second lossy step.
But I mean there is definitely far-future biopunk options eg. I'd guess it's easy to create some kind of solar panel organism which grows silicon crystals instead of using chlorophyll.
Fully agree - if the dog were only trying to get biscuits, it wouldn't continue to sit later on in it's life when you are no longer rewarding that behavior.Training dogs is actually some mix of the dog consciously expecting a biscuit, and raw updating on the actions previously taken.Hear sit -> Get biscuit -> feel goodbecomesHear sit -> Feel good -> get biscuit -> feel goodbecomesHear sit -> feel goodAt which point the dog likes sitting, it even reinforces itself, you can stop giving biscuits and start training something else
This is a good post, definitely shows that these concepts are confused. In a sense both examples are failures of both inner and outer alignment -
Also, the choice to train the AI on pull requests at all is in a sense an outer alignment failure.
If we could use negentropy as a cost, rather than computation time or energy use, then the system would be genuinely bounded.
Gender seems unusually likely to have many connotations & thus redundant representations in the model. What if you try testing some information the model has inferred, but which is only ever used for one binary query? Something where the model starts off not representing that thing, then if it represents it perfectly it will only ever change one type of thing. Like idk, whether or not the text is British or American English? Although that probably has some other connotations. Or whether or not the form of some word (lead or lead) is a verb or a noun.
Agree that gender is a more useful example, just not one tha necessarily provides clarity.
Yeah I think this is the fundamental problem. But it's a very simple way to state it. Perhaps useful for someone who doesn't believe ai alignment is a problem?
Here's my summary: Even at the limit of the amount of data & variety you can provide via RLHF, when the learned policy generalizes perfectly to all new situations you can throw at it, the result will still almost certainly be malign because there are still near infinite such policies, and they each behave differently on the infinite remaining types of situation you didn't manage to train it on yet. Because the particular policy is just one of many, it is unlikely to be correct.
But more importantly, behavior upon self improvement and reflection is likely something we didn't test. Because we can't. The alignment problem now requires we look into the details of generalization. This is where all the interesting stuff is.
Respect for thinking about this stuff yourself. You seem new to alignment (correct me if I'm wrong) - I think it might be helpful to view posting as primarily about getting feedback rather than contributing directly, unless you have read most of the other people's thoughts on whichever topic you are thinking/writing about.