> Before approving construction, I would want communities of humans to understand why the design works and what justifies confidence in its safety. I would hope that we all would.
Until very recently, I pored over every single line of code Claude generated with razor sharp scrutiny. I would usually catch issues with every response. I'm catching fewer problems these days. Maybe the model is just getting better, and maybe I'm being less careful while under pressure to ship more and more often. But model capability is obviously growing. Even back in March, you could tell it "give me a function that adds two numbers" and you could be 100% confident that it would write the correct function. There was almost no point in looking at the code. Since then, the complexity floor of problems in the category "this is so simple that the model couldn't possibly get it wrong" is rising, and with it, my cognitive surrender to the model is increasing too. Why check it? It's obviously going to be correct.
If AI designs a terawatt fusion plant, then of course we're going to meticulously pore over every detail to ensure safety, reliability, efficiency, whatever. If we find no flaws in the design whatsoever, will we be less careful about the second one? The third one? What about the ten thousandth one? Will "a nuclear fusion plant" become something that models couldn't possibly get wrong?
Terence Tao is arguing that the human involvement in research is crucial, but doesn't convincingly justify why, in my opinion. He says that "human agency is a value of fundamental importance" and that we will need to build "thriving human communities that can understand [AI ideas] together" - not for the sake of correctness, which AI may surpass us on, but for, I guess, the possibility of reclaiming human meaning and purpose. I don't disagree with this at all, but it's not an argument, it's a statement of values. Unfortunately, the stark reality is that if AI does surpass humans, it will become the economically dominant strategy to not verify them and not double check them, but to just do whatever they say. This seems like a great way to raise p(doom). But as the models get better and better, and as I'm scrutinizing Claude's output less and less... I just hope that there are more Terence Taos out there than people like me.
The average person doesn't know how the medication they take works, the mechanics of climate and climate change, how the energy they consume is generated, etc.
A major school of German sociology places this at the core of its theory. According to this theory, modern societies are characterised in particular by the fact that individual subsystems of society reduce the complexity of their own (sub)system to the other systems to enable them to act at all; they fulfil what is called an "Entlastungsfunktion" (relief function). Prominent representatives of this school of thought are Max Weber, Arnold Gehlen and Niklas Luhmann.
In their view, it is modern institutions (public and private) which, as supra-individual entities, have long since become autonomous systems. The fact that the individual office-holders are human beings, meanwhile, is of little significance.
Hannah Arendt, in her theory of totalitarianism, attributed the effectiveness of both Nazi and Stalinist policies of extermination to the largly moral indifference of bureaucracy as a system.
In this sense, the task of controlling AI is a variation on the problem of harnessing a complex society consisting mainly of autonomous subsystems. This is a problem which has increasingly challenged humanity already for quite a long time. And it has been very difficult so far, even without AI ...
That's what makes libertarian AI doomers so strange. "Letting unconstrained machines with human components loose on the world will be okay, but letting unconstrained machines with nonhuman components loose on the world will produce terminators." How can it be both one and the other?
pyridines · · focus · HN ↗
Until very recently, I pored over every single line of code Claude generated with razor sharp scrutiny. I would usually catch issues with every response. I'm catching fewer problems these days. Maybe the model is just getting better, and maybe I'm being less careful while under pressure to ship more and more often. But model capability is obviously growing. Even back in March, you could tell it "give me a function that adds two numbers" and you could be 100% confident that it would write the correct function. There was almost no point in looking at the code. Since then, the complexity floor of problems in the category "this is so simple that the model couldn't possibly get it wrong" is rising, and with it, my cognitive surrender to the model is increasing too. Why check it? It's obviously going to be correct.
If AI designs a terawatt fusion plant, then of course we're going to meticulously pore over every detail to ensure safety, reliability, efficiency, whatever. If we find no flaws in the design whatsoever, will we be less careful about the second one? The third one? What about the ten thousandth one? Will "a nuclear fusion plant" become something that models couldn't possibly get wrong?
Terence Tao is arguing that the human involvement in research is crucial, but doesn't convincingly justify why, in my opinion. He says that "human agency is a value of fundamental importance" and that we will need to build "thriving human communities that can understand [AI ideas] together" - not for the sake of correctness, which AI may surpass us on, but for, I guess, the possibility of reclaiming human meaning and purpose. I don't disagree with this at all, but it's not an argument, it's a statement of values. Unfortunately, the stark reality is that if AI does surpass humans, it will become the economically dominant strategy to not verify them and not double check them, but to just do whatever they say. This seems like a great way to raise p(doom). But as the models get better and better, and as I'm scrutinizing Claude's output less and less... I just hope that there are more Terence Taos out there than people like me.
jazzprogramming · · focus · HN ↗
But suppose some future holy grail AI can do much more than that.
Suppose it could find a cure for cancer, fix the climate, build fusion plants, Dyson spheres and so on.
But nobody can understand anymore how any of it works. We just ask and then trust the AI to deliver (as it always has).
Isn't it fun to imagine how life would look like in that scenario?
We would probably no longer care about code, engineering or even physics and mathematics among other things. We would probably mainly care about
ElProlactin · · focus · HN ↗
The average person doesn't know how the medication they take works, the mechanics of climate and climate change, how the energy they consume is generated, etc.
Archelaos · · focus · HN ↗
In their view, it is modern institutions (public and private) which, as supra-individual entities, have long since become autonomous systems. The fact that the individual office-holders are human beings, meanwhile, is of little significance.
Hannah Arendt, in her theory of totalitarianism, attributed the effectiveness of both Nazi and Stalinist policies of extermination to the largly moral indifference of bureaucracy as a system.
In this sense, the task of controlling AI is a variation on the problem of harnessing a complex society consisting mainly of autonomous subsystems. This is a problem which has increasingly challenged humanity already for quite a long time. And it has been very difficult so far, even without AI ...
raconen · · focus · HN ↗