ErickBall

I am currently a nuclear engineer with a focus in nuclear plant safety and probabilistic risk assessment. I am also an aspiring EA, interested in X-risk mitigation and the intersection of science and policy.

Wiki Contributions

Comments

The math doesn't necessarily work out that way. If you value the good stuff linearly, the optimal course of action will either be to spend all your resources right away (because the high discount rate makes the future too risky) or to save everything for later (because you can get such a high return on investment that spending any now would be wasteful). Even in a more realistic case where utility is logarithmic with, for example, computation, anticipation of much higher efficiency in the far future could lead to the optimal choice being to use essentially the bare minimum right now.

I think there are reasonable arguments for putting some resources toward a good life in the present, but they mostly involve not being able to realistically pull off total self-deprivation for an extended period of time. So finding the right balance is difficult, because our thinking is naturally biased to want to enjoy ourselves right now. How do you "cancel out" this bias while still accounting for the limits of your ability to maintain motivation? Seems like a tall order to achieve just by introspection.

Positive externalities is a bit of an odd way to phrase it--if it's just counting up the economic value (i.e. price) of the fossil fuels, doesn't it also disregard the consumer surplus? In other words, they've demonstrated that the negative externalities of pollution outweigh the value added on the margin, but if we were to radically decrease our usage of fossil fuels then the cost of energy (especially for certain uses with no good substitute, as you discussed above) would go way up, and the tradeoff on the margin would look very different.

I see your point about guilt/blame, but I'm just not sure the term we use to describe the phenomenon is the problem. We've already switched terms once (from "global warming" to "climate change") to sound more neutral, and I would argue that "climate change" is about the most neutral description possible--it doesn't imply that the change is good or bad, or suggest a cause. "Accidental terraforming", on the other hand, combined two terms with opposite valence, perhaps in the intent that they will cancel out? Terraforming is supposed to describe a desirable (for humans) change to the environment, while an accident is usually bad.

But the controversy, blame, and anger don't arise from the moniker, they are a natural consequence of trying to change behavior. In fact, people now like to say "anthropogenic climate change" precisely because they intend to put the blame explicitly on polluting industry. How can we take control of our effects on the climate if we don't first acknowledge them, and then add a moral valence? Without a "should", there is no impetus to action. Telling people they should do something different (and costly) will upset them, yes, but then you can't make an omelet without breaking some eggs.

How would a language model determine whether it has internet access? Naively, it seems like any attempt to test for internet access is doomed because if the model generates a query, it will also generate a plausible response to that query if one is not returned by an API. This could be fixed with some kind of hard coded internet search protocol (as they presumably implemented for Bing), but without it the LLM is in the dark, and a larger or more competent model should be no more likely to understand that it has no internet access.

If the NRO had Sentient in 2012 then it wasn't even a deep learning system. Probably they have something now that's built from transformers (I know other government agencies are working on things like this for their own domain specific purposes). But it's got to be pretty far behind the commercial state of the art, because government agencies don't have the in house expertise or the budget flexibility to move quickly on large scale basic research.

Those are... mostly not AI problems? People like to use kitchen-based tasks because current robots are not great at dealing with messy environments, and because a kitchen is an environment heavily optimized for the specific physical and visuospatial capabilities of humans. That makes doing tasks in a random kitchen seem easy to humans, while being difficult for machines. But it isn't reflective of real world capabilities.

When you want to automate a physical task, you change the interface and the tools to make it more machine friendly. Building a roomba is ten times easier than building a robot that can navigate a house while operating an arbitrary stick vacuum. If you want dishes cleaned with minimal human input, you build a dishwasher that doesn't require placing each dish carefully in a rack (eg https://youtube.com/watch?v=GiGAwfAZPo0).

Some people have it in their heads that AI is not transformative or is no threat to humans unless it can also do all the exact physical tasks that humans can do. But a key feature of intelligence is that you can figure out ways to avoid doing the parts that are hardest for you, and still accomplish your high level goals.

"Unaligned AGI doesn't take over the world by killing us - it takes over the world by seducing us."

Por que no los dos?

Thanks, some of those possibilities do seem quite risky and I hadn't thought about them before.

It looks like in that thread you never replied to the people saying they couldn't follow your explanation. Specifically, what bad things could an AI regulator do that would increase the probability of doom?

Load More