Artificial Intelligence

Concrete Problems in AI Safety

Earlier this week, Google Research (in collaboration with scientists at OpenAI, Stanford and Berkeley) released Concrete Problems in AI Safety, which outlines five technical problems related to accident risk in AI systems. Four of the authors are friends and technical advisors of the Open Philanthropy Project.

We’re very excited about this paper. We highly recommend it to anyone looking to get a sense for the tractability of reducing potential risks from advanced AI (a cause we’ve previously written about) - as well as for what sorts of research we would be most excited to fund in this cause.

Potential Risks from Advanced Artificial Intelligence: The Philanthropic Opportunity

We’re planning to make potential risks from artificial intelligence a major priority this year. We feel this cause presents an outstanding philanthropic opportunity — with extremely high importance, high neglectedness, and reasonable tractability (our three criteria for causes) — for someone in our position. We believe that the faster we can get fully up to speed on key issues and explore the opportunities we currently see, the faster we can lay the groundwork for informed, effective giving both this year and in the future.

With all of this in mind, we’re placing a larger “bet” on this cause, this year, than we are placing even on other focus areas — not necessarily in terms of funding (we aren’t sure we’ll identify very large funding opportunities this year, and are more focused on laying the groundwork for future years), but in terms of senior staff time, which at this point is a scarcer resource for us. Consistent with our philosophy of hits-based giving, we are doing this not because we have confidence in how the future will play out and how we can impact it, but because we see a risk worth taking. In about a year, we’ll formally review our progress and reconsider how senior staff time is allocated.

This post will first discuss why I consider this cause to be an outstanding philanthropic opportunity. (My views are fairly representative, but not perfectly representative, of those of other staff working on this cause.) It will then give a broad outline of our planned activities for the coming year, some of the key principles we hope to follow in this work, and some of the risks and reservations we have about prioritizing this cause as highly as we are.

In brief:

  • It seems to me that artificial intelligence is currently on a very short list of the most dynamic, unpredictable, and potentially world-changing areas of science. I believe there’s a nontrivial probability that transformative AI will be developed within the next 20 years, with enormous global consequences.
  • By and large, I expect the consequences of this progress — whether or not transformative AI is developed soon — to be positive. However, I also perceive risks. Transformative AI could be a very powerful technology, with potentially globally catastrophic consequences if it is misused or if there is a major accident involving it. Because of this, I see this cause as having extremely high importance (one of our key criteria), even while accounting for substantial uncertainty about the likelihood of developing transformative AI in the coming decades and about the size of the risks. I discuss the nature of potential risks below; note that I think they do not apply to today’s AI systems.
  • I consider this cause to be highly neglected in important respects. There is a substantial and growing field around artificial intelligence and machine learning research, but most of it is not focused on reducing potential risks. We’ve put substantial work into trying to ensure that we have a thorough landscape of the researchers, funders, and key institutions whose work is relevant to potential risks from advanced AI. We believe that the amount of work being done is well short of what it productively could be (despite recent media attention); that philanthropy could be helpful; and that the activities we’re considering wouldn’t be redundant with those of other funders.
  • I believe that there is useful work to be done today in order to mitigate future potential risks. In particular, (a) I think there are important technical problems that can be worked on today, that could prove relevant to reducing accident risks; (b) I preliminarily feel that there is also considerable scope for analysis of potential strategic and policy considerations.
  • More broadly, the Open Philanthropy Project may be able to help support an increase in the number of people – particularly people with strong relevant technical backgrounds - thinking through how to reduce potential risks, which could be important in the future even if the work done in the short term does not prove essential. I believe that one of the things philanthropy is best-positioned to do is provide steady, long-term support as fields and institutions grow.
  • I consider this a challenging cause. I think it would be easy to do harm while trying to do good. For example, trying to raise the profile of potential risks could contribute (and, I believe, has contributed to some degree) to non-nuanced or inaccurate portrayals of risk in the media, which in turn could raise the risks of premature and/or counterproductive regulation. I consider the Open Philanthropy Project relatively well-positioned to work in this cause while being attentive to pitfalls, and to deeply integrate people with strong technical expertise into our work.
  • I see much room for debate in the decision to prioritize this cause as highly as we are. However, I think it is important that a philanthropist in our position be willing to take major risks, and prioritizing this cause is a risk that I see as very worth taking.

My views on this cause have evolved considerably over time. I will discuss the evolution of my thinking in detail in a future post, but this post focuses on the case for prioritizing this cause today.

Some Background on Our Views Regarding Advanced Artificial Intelligence

We’re planning to make potential risks from advanced artificial intelligence a major priority in 2016. A future post will discuss why; this post gives some background.

Summary:

  • I first give our definition of “transformative artificial intelligence,” our term for a type of potential advanced artificial intelligence we find particularly relevant for our purposes. Roughly and conceptually, transformative AI refers to potential future AI that precipitates a transition comparable to (or more significant than) the agricultural or industrial revolution. I also provide (below) a more detailed definition. The concept of “transformative AI” has some overlap with concepts put forth by others, such as “superintelligence” and “artificial general intelligence.” However, “transformative AI” is intended to be a more inclusive term, leaving open the possibility of AI systems that count as “transformative” despite lacking many abilities humans have.
  • I then discuss the question of whether, and when, we might expect transformative AI to be developed. This question has many properties (long timelines, relatively vague concepts, lack of detailed public analysis) I associate with developments that are nearly impossible to forecast, and I don’t think it is possible to make high-certainty forecasts on the matter. With that said, I am comfortable saying that I think there is a nontrivial likelihood (at least 10% with moderate robustness, and at least 1% with high robustness) of transformative AI within the next 20 years. I can’t feasibly share all of the information that goes into this view, but I try to outline the general process I have followed to reach it.
  • Finally, I briefly discuss whether there are other potential future developments that seem to have similar potential for impact on similar timescales to transformative AI, in order to put our interest in AI in context.

The ideas in this post overlap with some arguments made by others, but I think it is important to lay out the specific views on these issues that I endorse. Note that this post is confined in scope to the above topics; it does not, for example, discuss potential risks associated with AI or potential measures for reducing them. I will discuss the latter topics more in the future.

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