Surrogate goals might be one of the most promising approaches to reduce (the disvalue resulting from) threats. The idea is to add to one’s current goals a surrogate goal that one did not initially care about, hoping that any potential threats will target this surrogate goal rather than what one initially cared about.
In this post, I will outline two key obstacles to a successful implementation of surrogate goals.
To steer the development of powerful AI in beneficial directions, we need an accurate understanding of how the transition to a world with powerful AI systems will unfold. A key question is how long such a transition (or “takeoff”) will take.
Summary This post assumes deep familiarity with the ideas discussed in Caspar Oesterheld’s paper Multiverse-wide cooperation via coordinated decision-making. I wrote a short introduction to multiverse-wide cooperation in an earlier post (but I still recommend reading parts of Caspar’s original paper first, or at least this more advanced introduction, because some of the things that […]
Agents that threaten to harm other agents, either in an attempt at extortion or as part of an escalating conflict, are an important form of agential s-risks. To avoid worst-case outcomes resulting from the execution of such threats, I suggest that agents add a “meaningless” surrogate goal to their utility function.
This is a post I wrote about Caspar Oesterheld’s long paper Multiverse-wide cooperation via coordinated decision-making. Because I have found the idea tricky to explain – which unfortunately makes it difficult to get feedback from others on whether the thinking behind it makes sense – I decided to write a shorter summary. While I am […]
In the essay Reducing Risks of Astronomical Suffering: A Neglected Priority, s-risks (also called suffering risks or risks of astronomical suffering) are defined as “events that would bring about suffering on an astronomical scale, vastly exceeding all suffering that has existed on Earth so far”.
Efforts to shape advanced artificial intelligence (AI) may be among the most promising altruistic endeavours. If the transition to advanced AI goes wrong, the worst outcomes may involve not only the end of human civilization, but also astronomical amounts of suffering – a so-called s-risk.
This is a reply to Metzinger’s essay on Benevolent Artificial Anti-natalism (BAAN), which appeared on EDGE.org (7.8.2017). Metzinger invites us to consider a hypothetical scenario where smarter-than-human artificial intelligence (AI) is built with the goal of assisting us with ethical deliberation. Being superior to us in its understanding of how our own minds function, the […]
Suppose you investigated two interventions A and B and came up with estimates for how much impact A and B will have. Your best guess is that A will spare a billion sentient beings from suffering, while B “only” spares a thousand beings. Now, should you actually believe that A is many orders of magnitude more effective than B?
This post analyses key strategic questions on moral advocacy, such as:
What does moral advocacy look like in practice? Which values should we spread, and how?
How effective is moral advocacy compared to other interventions such as directly influencing new technologies?
What are the most important arguments for and against focusing on moral advocacy?
Efforts to mitigate the risks of advanced artificial intelligence may be a top priority for effective altruists. If this is true, what are the best means to shape AI? Should we write math-heavy papers on open technical questions, or opt for broader, non-technical interventions like values spreading?
This post is a discussion between Lukas Gloor and Tobias Baumann on the meaning of tool use and intelligence, which is relevant to our thinking about the future or (artificial) intelligence and the likelihood of AI scenarios.
Imagine a data set of images labeled “suffering” or “no suffering”. For instance, suppose the “suffering” category contains documentations of war atrocities or factory farms, and the “no suffering” category contains innocuous images – say, a library. We could then use a neural network or other machine learning algorithms to learn to detect suffering based on that data.
This post is based on notes for a talk I gave at EAG Boston 2017. I talk about risks of severe suffering in the far future, or s-risks. Reducing these risks is the main focus of the Foundational Research Institute, the EA research group that I represent.
We were moved by the many good reasons to make conversations public. At the same time, we felt the content we wanted to publish differed from the articles on our main site. Hence, we're happy to announce the launch of FRI’s new blog.