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A number of major mid-year MIRI updates: we received our largest donation to date, $1.01 million from an Ethereum investor! Our research priorities have also shifted somewhat, reflecting the addition of four new full-time researchers (Marcello Herreshoff, Sam Eisenstat, Tsvi Benson-Tilsen, and Abram Demski) and the departure of Patrick LaVictoire and Jessica Taylor.
Research updates
General updates
News and links
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We have several major announcements to make, covering new developments in the two months since our 2017 strategy update:
1. On May 30th, we received a surprise $1.01 million donation from an Ethereum cryptocurrency investor. This is the single largest contribution we have received to date by a large margin, and will have a substantial effect on our plans over the coming year.
2. Two new full-time researchers are joining MIRI: Tsvi Benson-Tilsen and Abram Demski. This comes in the wake of Sam Eisenstat and Marcello Herreshoff’s addition to the team in May. We’ve also begun working with engineers on a trial basis for our new slate of software engineer job openings.
3. Two of our researchers have recently left: Patrick LaVictoire and Jessica Taylor, researchers previously heading work on our “Alignment for Advanced Machine Learning Systems” research agenda.
For more details, see below.
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Research updates
General updates
News and links
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Research updates
General updates
News and links
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In our last strategy update (August 2016), Nate wrote that MIRI’s priorities were to make progress on our agent foundations agenda and begin work on our new “Alignment for Advanced Machine Learning Systems” agenda, to collaborate and communicate with other researchers, and to grow our research and ops teams.
Since then, senior staff at MIRI have reassessed their views on how far off artificial general intelligence (AGI) is and concluded that shorter timelines are more likely than they were previously thinking. A few lines of recent evidence point in this direction, such as:1
There’s no consensus among MIRI researchers on how long timelines are, and our aggregated estimate puts medium-to-high probability on scenarios in which the research community hasn’t developed AGI by, e.g., 2035. On average, however, research staff now assign moderately higher probability to AGI’s being developed before 2035 than we did a year or two ago. This has a few implications for our strategy:
1. Our relationships with current key players in AGI safety and capabilities play a larger role in our strategic thinking. Short-timeline scenarios reduce the expected number of important new players who will enter the space before we hit AGI, and increase how much influence current players are likely to have.
2. Our research priorities are somewhat different, since shorter timelines change what research paths are likely to pay out before we hit AGI, and also concentrate our probability mass more on scenarios where AGI shares various features in common with present-day machine learning systems.
Both updates represent directions we’ve already been trending in for various reasons.3 However, we’re moving in these two directions more quickly and confidently than we were last year. As an example, Nate is spending less time on staff management and other administrative duties than in the past (having handed these off to MIRI COO Malo Bourgon) and less time on broad communications work (having delegated a fair amount of this to me), allowing him to spend more time on object-level research, research prioritization work, and more targeted communications.4
I’ll lay out what these updates mean for our plans in more concrete detail below.
The Machine Intelligence Research Institute is looking for highly capable software engineers to directly support our AI alignment research efforts, with a focus on projects related to machine learning. We’re seeking engineers with strong programming skills who are passionate about MIRI’s mission and looking for challenging and intellectually engaging work.
While our goal is to hire full-time, we are initially looking for paid interns. Successful internships may then transition into staff positions.
The start time for interns is flexible, but we’re aiming for May or June. We will likely run several batches of internships, so if you are interested but unable to start in the next few months, do still apply. The length of the internship is flexible, but we’re aiming for 2–3 months.
Examples of the kinds of work you’ll do during the internship:
For MIRI, the benefit of this program is that it’s a great way to get to know you and assess you for a potential hire. For applicants, the benefits are that this is an excellent opportunity to get your hands dirty and level up your machine learning skills, and to get to the cutting edge of the AI safety field, with a potential to stay in a full-time engineering role after the internship concludes.
Our goal is to trial many more people than we expect to hire, so our threshold for keeping on engineers long-term as full staff will be higher than for accepting applicants to our internship.
Some qualities of the ideal candidate:
We strive to make working at MIRI a rewarding experience.
MIRI is an equal opportunity employer. We are committed to making employment decisions based on merit and value. This commitment includes complying with all federal, state, and local laws. We desire to maintain a work environment free of harassment or discrimination due to sex, race, religion, color, creed, national origin, sexual orientation, citizenship, physical or mental disability, marital status, familial status, ethnicity, ancestry, status as a victim of domestic violence, age, or any other status protected by federal, state, or local laws.
If interested, click here to apply. For questions or comments, email engineering@intelligence.org.
Update (December 2017): We’re now putting less emphasis on finding interns and looking for highly skilled engineers available for full-time work. Updated job post here.
I recently gave a talk at Google on the problem of aligning smarter-than-human AI with operators’ goals:
The talk was inspired by “AI Alignment: Why It’s Hard, and Where to Start,” and serves as an introduction to the subfield of alignment research in AI. A modified transcript follows.
Talk outline (slides):
1. Overview
2. Simple bright ideas going wrong
2.1. Task: Fill a cauldron
2.2. Subproblem: Suspend buttons3.1. Alignment priorities
3.2. Four key propositions
Nate Soares’ recent decision theory paper with Ben Levinstein, “Cheating Death in Damascus,” prompted some valuable questions and comments from an acquaintance (anonymized here). I’ve put together edited excerpts from the commenter’s email below, with Nate’s responses.
The discussion concerns functional decision theory (FDT), a newly proposed alternative to causal decision theory (CDT) and evidential decision theory (EDT). Where EDT says “choose the most auspicious action” and CDT says “choose the action that has the best effects,” FDT says “choose the output of one’s decision algorithm that has the best effects across all instances of that algorithm.”
FDT usually behaves similarly to CDT. In a one-shot prisoner’s dilemma between two agents who know they are following FDT, however, FDT parts ways with CDT and prescribes cooperation, on the grounds that each agent runs the same decision-making procedure, and that therefore each agent is effectively choosing for both agents at once.1
Below, Nate provides some of his own perspective on why FDT generally achieves higher utility than CDT and EDT. Some of the stances he sketches out here are stronger than the assumptions needed to justify FDT, but should shed some light on why researchers at MIRI think FDT can help resolve a number of longstanding puzzles in the foundations of rational action.
Anonymous: This is great stuff! I’m behind on reading loads of papers and books for my research, but this came across my path and hooked me, which speaks highly of how interesting is the content and the sense that this paper is making progress.
My general take is that you are right that these kinds of problems need to be specified in more detail. However, my guess is that once you do so, game theorists would get the right answer. Perhaps that’s what FDT is: it’s an approach to clarifying ambiguous games that leads to a formalism where people like Pearl and myself can use our standard approaches to get the right answer.
I know there’s a lot of inertia in the “decision theory” language, so probably it doesn’t make sense to change. But if there were no such sunk costs, I would recommend a different framing. It’s not that people’s decision theories are wrong; it’s that they are unable to correctly formalize problems in which there are high-performance predictors. You show how to do that, using the idea of intervening on (i.e., choosing between putative outputs of) the algorithm, rather than intervening on actions. Everything else follows from a sufficiently precise and non-contradictory statement of the decision problem.
Probably the easiest move this line of work could make to ease this knee-jerk response of mine in defense of mainstream Bayesian game theory is to just be clear that CDT is not meant to capture mainstream Bayesian game theory. Rather, it is a model of one response to a class of problems not normally considered and for which existing approaches are ambiguous.
Nate Soares: I don’t take this view myself. My view is more like: When you add accurate predictors to the Rube Goldberg machine that is the universe — which can in fact be done — the future of that universe can be determined by the behavior of the algorithm being predicted. The algorithm that we put in the “thing-being-predicted” slot can do significantly better if its reasoning on the subject of which actions to output respects the universe’s downstream causal structure (which is something CDT and FDT do, but which EDT neglects), and it can do better again if its reasoning also respects the world’s global logical structure (which is done by FDT alone).
We don’t know exactly how to respect this wider class of dependencies in general yet, but we do know how to do it in many simple cases. While it agrees with modern decision theory and game theory in many simple situations, its prescriptions do seem to differ in non-trivial applications.
The main case where we can easily see that FDT is not just a better tool for formalizing game theorists’ traditional intuitions is in prisoner’s dilemmas. Game theory is pretty adamant about the fact that it’s rational to defect in a one-shot PD, whereas two FDT agents facing off in a one-shot PD will cooperate.
In particular, classical game theory employs a “common knowledge of shared rationality” assumption which, when you look closely at it, cashes out more or less as “common knowledge that all parties are using CDT and this axiom.” Game theory where common knowledge of shared rationality is defined to mean “common knowledge that all parties are using FDT and this axiom” gives substantially different results, such as cooperation in one-shot PDs.
