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The Machine Intelligence Research Institute’s 2015 winter fundraising drive begins today, December 1! Our current progress:
The drive will run for the month of December, and will help support MIRI’s research efforts aimed at ensuring that smarter-than-human AI systems have a positive impact.
MIRI’s Research Focus
The field of AI has a goal of automating perception, reasoning, and decision-making — the many abilities we group under the label “intelligence.” Most leading researchers in AI expect our best AI algorithms to begin strongly outperforming humans this century in most cognitive tasks. In spite of this, relatively little time and effort has gone into trying to identify the technical prerequisites for making smarter-than-human AI systems safe and useful.
We believe that several basic theoretical questions will need to be answered in order to make advanced AI systems stable, transparent, and error-tolerant, and in order to specify correct goals for such systems. Our technical agenda describes what we think are the most important and tractable of these questions.
Smarter-than-human AI may be 50 years or more away. There are a number of reasons we nonetheless consider it important to begin work on these problems today:
- High capability ceilings — Humans appear to be nowhere near physical limits for cognitive ability, and even modest advantages in intelligence may yield decisive strategic advantages for AI systems.
- “Sorcerer’s Apprentice” scenarios — Smarter AI systems can come up with increasingly creative ways to meet programmed goals. The harder it is to anticipate how a goal will be achieved, the harder it is to specify the correct goal.
- Convergent instrumental goals — By default, highly capable decision-makers are likely to have incentives to treat human operators adversarially.
- AI speedup effects — Progress in AI is likely to accelerate as AI systems approach human-level proficiency in skills like software engineering.
We think MIRI is well-positioned to make progress on these problems for four reasons: our initial technical results have been promising (see our publications), our methodology has a good track record of working in the past (see MIRI’s Approach), we have already had a significant influence on the debate about long-run AI outcomes (see Assessing Our Past and Potential Impact), and we have an exclusive focus on these issues (see What Sets MIRI Apart?). MIRI is currently the only organization specializing in long-term technical AI safety research, and our independence from industry and academia allows us to effectively address gaps in other institutions’ research efforts.
General Progress This Year
In June, Luke Muehlhauser left MIRI for a research position at the Open Philanthropy Project. I replaced Luke as MIRI’s Executive Director, and I’m happy to say that the transition has gone well. We’ve split our time between technical research and academic outreach, running a workshop series aimed at introducing a wider scientific audience to our work and sponsoring a three-week summer fellows program aimed at training skills required to do groundbreaking theoretical research.
Our fundraiser this summer was our biggest to date. We raised a total of $631,957 from 263 distinct donors, smashing our previous funding drive record by over $200,000. Medium-sized donors stepped up their game to help us hit our first two funding targets: many more donors gave between $5,000 and $50,000 than in past fundraisers. Our successful fundraisers, workshops, and fellows program have allowed us to ramp up our growth substantially, and have already led directly to several new researcher hires.
2015 has been an astounding year for AI safety engineering. In January, the Future of Life Institute brought together the leading organizations studying long-term AI risk and top AI researchers in academia and industry for a “Future of AI” conference in San Juan, Puerto Rico. Out of this conference came a widely endorsed open letter, accompanied by a research priorities document drawing heavily on MIRI’s work. Two prominent AI scientists who helped organize the event, Stuart Russell and Bart Selman, have since become MIRI research advisors (in June and July, respectively). The conference also resulted in an AI safety grants program, with MIRI receiving some of the largest grants.
In addition to the FLI conference, we’ve spoken this year at AAAI-15, AGI-15, LORI 2015, EA Global, the American Physical Society, and the leading U.S. science and technology think tank, ITIF. We also co-organized a decision theory conference at Cambridge University and ran a ten-week seminar series at UC Berkeley.
Three new full-time research fellows have joined our team this year: Patrick LaVictoire in March, Jessica Taylor in August, and Andrew Critch in September. Scott Garrabrant will become our newest research fellow this month, after having made major contributions as a workshop attendee and research associate.
Meanwhile, our two new research interns, Kaya Stechly and Rafael Cosman, have been going through old results and consolidating and polishing material into new papers; and three of our new research associates, Vadim Kosoy, Abram Demski, and Tsvi Benson-Tilsen, have been producing a string of promising results on our research forum. Another intern, Jack Gallagher, contributed to our type theory project over the summer.
To accommodate our growing team, we’ve recently hired a new office manager, Andrew Lapinski-Barker, and will be moving into a larger office space this month. On the whole, I’m very pleased with our new academic collaborations, outreach efforts, and growth.
Research Progress This Year
As our research projects and collaborations have multiplied, we’ve made more use of online mechanisms for quick communication and feedback between researchers. In March, we launched the Intelligent Agent Foundations Forum, a discussion forum for AI alignment research. Many of our subsequent publications have been developed from material on the forum, beginning with Patrick LaVictoire’s “An introduction to Löb’s theorem in MIRI’s research.”
We have also produced a number of new papers in 2015 and, most importantly, arrived at new research insights.
In July, we revised our primary technical agenda paper for 2016 publication. Our other new publications and results can be categorized by their place in the research agenda:
We’ve been exploring new approaches to the problems of naturalized induction and logical uncertainty, with early results published in various venues, including Fallenstein et al.’s “Reflective oracles” (presented in abridged form at LORI 2015) and “Reflective variants of Solomonoff induction and AIXI” (presented at AGI-15), and Garrabrant et al.’s “Asymptotic logical uncertainty and the Benford test” (available on arXiv). We also published the overview papers “Formalizing two problems of realistic world-models” and “Questions of reasoning under logical uncertainty.”
In decision theory, Patrick LaVictoire and others have developed new results pertaining to bargaining and division of trade gains, using the proof-based decision theory framework (example). Meanwhile, the team has been developing a better understanding of the strengths and limitations of different approaches to decision theory, an effort spearheaded by Eliezer Yudkowsky, Benya Fallenstein, and me, culminating in some insights that will appear in a paper next year. Andrew Critch has proved some promising results about bounded versions of proof-based decision-makers, which will also appear in an upcoming paper. Additionally, we presented a shortened version of our overview paper at AGI-15.
In Vingean reflection, Benya Fallenstein and Research Associate Ramana Kumar collaborated on “Proof-producing reflection for HOL” (presented at ITP 2015) and have been working on an FLI-funded implementation of reflective reasoning in the HOL theorem prover. Separately, the reflective oracle framework has helped us gain a better understanding of what kinds of reflection are and are not possible, yielding some nice technical results and a few insights that seem promising. We also published the overview paper “Vingean reflection.”
Jessica Taylor, Benya Fallenstein, and Eliezer Yudkowsky have focused on error tolerance on and off throughout the year. We released Taylor’s “Quantilizers” (accepted to a workshop at AAAI-16) and presented the paper “Corrigibility” at a AAAI-15 workshop.
In value specification, we published the AAAI-15 workshop paper “Concept learning for safe autonomous AI” and the overview paper “The value learning problem.” With support from an FLI grant, Jessica Taylor is working on better formalizing subproblems in this area, and has recently begun writing up her thoughts on this subject on the research forum.
Lastly, in forecasting and strategy, we published “Formalizing convergent instrumental goals” (accepted to a AAAI-16 workshop) and two historical case studies: “The Asilomar Conference” and “Leó Szilárd and the danger of nuclear weapons.” Many other strategic analyses have been posted to the recently revamped AI Impacts site, where Katja Grace has been publishing research about patterns in technological development.
Fundraiser Targets and Future Plans
Like our last fundraiser, this will be a non-matching fundraiser with multiple funding targets our donors can choose between to help shape MIRI’s trajectory. Our successful summer fundraiser has helped determine how ambitious we’re making our plans; although we may still slow down or accelerate our growth based on our fundraising performance, our current plans assume a budget of roughly $1,825,000 per year.
Of this, about $100,000 is being paid for in 2016 through FLI grants, funded by Elon Musk and the Open Philanthropy Project. The rest depends on our fundraising and grant-writing success. We have a twelve-month runway as of January 1, which we would ideally like to extend.
Taking all of this into account, our winter funding targets are:
Target 1 — $150k: Holding steady. At this level, we would have enough funds to maintain our runway in early 2016 while continuing all current operations, including running workshops, writing papers, and attending conferences.
Target 2 — $450k: Maintaining MIRI’s growth rate. At this funding level, we would be much more confident that our new growth plans are sustainable, and we would be able to devote more attention to academic outreach. We would be able to spend less staff time on fundraising in the coming year, and might skip our summer fundraiser.
Target 3 — $1M: Bigger plans, faster growth. At this level, we would be able to substantially increase our recruiting efforts and take on new research projects. It would be evident that our donors’ support is stronger than we thought, and we would move to scale up our plans and growth rate accordingly.
Target 4 — $6M: A new MIRI. At this point, MIRI would become a qualitatively different organization. With this level of funding, we would be able to diversify our research initiatives and begin branching out from our current agenda into alternative angles of attack on the AI alignment problem.
Our projected spending over the next twelve months, excluding earmarked funds for the independent AI Impacts project, breaks down as follows:
Our largest cost ($700,000) is in wages and benefits for existing research staff and contracted researchers, including research associates. Our current priority is to further expand the team. We expect to spend an additional $150,000 on salaries and benefits for new research staff in 2016, but that number could go up or down significantly depending on when new research fellows begin work:
- Mihály Bárász, who was originally slated to begin in November 2015, has delayed his start date due to unexpected personal circumstances. He plans to join the team in 2016.
- We are recruiting a specialist for our type theory in type theory project, which is aimed at developing simple programmatic models of reflective reasoners. Interest in this topic has been increasing recently, which is exciting; but the basic tools needed for our work are still missing. If you have programmer or mathematician friends who are interested in dependently typed programming languages and MIRI’s work, you can send them our application form.
- We are considering several other possible additions to the research team.
Much of the rest of our budget goes into fixed costs that will not need to grow much as we expand the research team. This includes $475,000 for administrator wages and benefits and $250,000 for costs of doing business. Our main cost of doing business is renting office space (slightly over $100,000).
Note that the boundaries between these categories are sometimes fuzzy. For example, my salary is included in the admin staff category, despite the fact that I spend some of my time on technical research (and hope to increase that amount in 2016).
Our remaining budget goes into organizing or sponsoring research events, such as fellows programs, MIRIx events, or workshops ($250,000). Some activities (e.g., traveling to conferences) are aimed at sharing our work with the larger academic community. Others, such as researcher retreats, are focused on solving open problems in our research agenda. After experimenting with different types of research staff retreat in 2015, we’re beginning to settle on a model that works well, and we’ll be running a number of retreats throughout 2016.
In past years, we’ve generally raised $1M per year, and spent a similar amount. Thanks to substantial recent increases in donor support, however, we’re in a position to scale up significantly.
Our donors blew us away with their support in our last fundraiser. If we can continue our fundraising and grant successes, we’ll be able to sustain our new budget and act on the unique opportunities outlined in Why Now Matters, helping set the agenda and build the formal tools for the young field of AI safety engineering. And if our donors keep stepping up their game, we believe we have the capacity to scale up our program even faster. We’re thrilled at this prospect, and we’re enormously grateful for your support.
MIRI Research Fellow Jessica Taylor has written a new paper on an error-tolerant framework for software agents, “Quantilizers: A safer alternative to maximizers for limited optimization.” Taylor’s paper will be presented at the AAAI-16 AI, Ethics and Society workshop. The abstract reads:
In the field of AI, expected utility maximizers are commonly used as a model for idealized agents. However, expected utility maximization can lead to unintended solutions when the utility function does not quantify everything the operators care about: imagine, for example, an expected utility maximizer tasked with winning money on the stock market, which has no regard for whether it accidentally causes a market crash. Once AI systems become sufficiently intelligent and powerful, these unintended solutions could become quite dangerous. In this paper, we describe an alternative to expected utility maximization for powerful AI systems, which we call expected utility quantilization. This could allow the construction of AI systems that do not necessarily fall into strange and unanticipated shortcuts and edge cases in pursuit of their goals.
Expected utility quantilization is the approach of selecting a random action in the top n% of actions from some distribution γ, sorted by expected utility. The distribution γ might, for example, be a set of actions weighted by how likely a human is to perform them. A quantilizer based on such a distribution would behave like a compromise between a human and an expected utility maximizer. The agent’s utility function directs it toward intuitively desirable outcomes in novel ways, making it potentially more useful than a digitized human; while γ directs it toward safer and more predictable strategies.
Quantilization is a formalization of the idea of “satisficing,” or selecting actions that achieve some minimal threshold of expected utility. Agents that try to pick good strategies, but not maximally good ones, seem less likely to come up with extraordinary and unconventional strategies, thereby reducing both the benefits and the risks of smarter-than-human AI systems. Designing AI systems to satisfice looks especially useful for averting harmful convergent instrumental goals and perverse instantiations of terminal goals:
- If we design an AI system to cure cancer, and γ labels it bizarre to reduce cancer rates by increasing the rate of some other terminal illness, them a quantilizer will be less likely to adopt this perverse strategy even if our imperfect specification of the system’s goals gave this strategy high expected utility.
- If superintelligent AI systems have a default incentive to seize control of resources, but γ labels these policies bizarre, then a quantilizer will be less likely to converge on these strategies.
Taylor notes that the quantilizing approach to satisficing may even allow us to disproportionately reap the benefits of maximization without incurring proportional costs, by specifying some restricted domain in which the quantilizer has low impact without requiring that it have low impact overall — “targeted-impact” quantilization.
One obvious objection to the idea of satisficing is that a satisficing agent might build an expected utility maximizer. Maximizing, after all, can be an extremely effective way to satisfice. Quantilization avoids this objection: maximizing and quantilizing may both be good ways to satisfice, but maximizing is not an effective way to quantilize. A quantilizer will rank many maximizing behaviors as “bizarre,” and will therefore avoid delegating its decisions to an agent that maximizes the quantilizer’s expected utility.
Taylor shows that the cost of relying on a 0.1-quantilizer (which selects a random action from the top 10% of actions), on expectation, is no more than 10 times that of relying on the recommendation of its distribution γ; the expected cost of relying on a 0.01-quantilizer (which selects from the top 1% of actions) is no more than 100 times that of relying on γ; and so on. Quantilization is optimal among the set of strategies that are low-cost in this respect.
However, expected utility quantilization is not a magic bullet. It depends strongly on how we specify the action distribution γ, and Taylor shows that ordinary quantilizers behave poorly in repeated games and in scenarios where “ordinary” actions in γ tend to have very high or very low expected utility. Further investigation is needed to determine if quantilizers (or some variant on quantilizers) can remedy these problems.
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Tsvi Benson-Tilsen, a MIRI associate and UC Berkeley PhD candidate, has written a paper with contributions from MIRI Executive Director Nate Soares on strategies that will tend to be useful for most possible ends: “Formalizing convergent instrumental goals.” The paper will be presented as a poster at the AAAI-16 AI, Ethics and Society workshop.
Steve Omohundro has argued that AI agents with almost any goal will converge upon a set of “basic drives,” such as resource acquisition, that tend to increase agents’ general influence and freedom of action. This idea, which Nick Bostrom calls the instrumental convergence thesis, has important implications for future progress in AI. It suggests that highly capable decision-making systems may pose critical risks even if they are not programmed with any antisocial goals. Merely by being indifferent to human operators’ goals, such systems can have incentives to manipulate, exploit, or compete with operators.
The new paper serves to add precision to Omohundro and Bostrom’s arguments, while testing the arguments’ applicability in simple settings. Benson-Tilsen and Soares write:
In this paper, we will argue that under a very general set of assumptions, intelligent rational agents will tend to seize all available resources. We do this using a model, described in section 4, that considers an agent taking a sequence of actions which require and potentially produce resources. […] The theorems proved in section 4 are not mathematically difficult, and for those who find Omohundro’s arguments intuitively obvious, our theorems, too, will seem trivial. This model is not intended to be surprising; rather, the goal is to give a formal notion of “instrumentally convergent goals,” and to demonstrate that this notion captures relevant aspects of Omohundro’s intuitions.
Our model predicts that intelligent rational agents will engage in trade and cooperation, but only so long as the gains from trading and cooperating are higher than the gains available to the agent by taking those resources by force or other means. This model further predicts that agents will not in fact “leave humans alone” unless their utility function places intrinsic utility on the state of human-occupied regions: absent such a utility function, this model shows that powerful agents will have incentives to reshape the space that humans occupy.
Benson-Tilsen and Soares define a universe divided into regions that may change in different ways depending on an agent’s actions. The agent wants to make certain regions enter certain states, and may collect resources from regions to that end. This model can illustrate the idea that highly capable agents nearly always attempt to extract resources from regions they are indifferent to, provided the usefulness of the resources outweighs the extraction cost.
The relevant models are simple, and make few assumptions about the particular architecture of advanced AI systems. This makes it possible to draw some general conclusions about useful lines of safety research even if we’re largely in the dark about how or when highly advanced decision-making systems will be developed. The most obvious way to avoid harmful goals is to incorporate human values into AI systems’ utility functions, a project outlined in “The value learning problem.” Alternatively (or as a supplementary measure), we can attempt to specify highly capable agents that violate Benson-Tilsen and Soares’ assumptions, avoiding dangerous behavior in spite of lacking correct goals. This approach is explored in the paper “Corrigibility.”
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In January, nearly 200 public intellectuals submitted essays in response to the 2015 Edge.org question, “What Do You Think About Machines That Think?” (available online). The essay prompt began:
In recent years, the 1980s-era philosophical discussions about artificial intelligence (AI)—whether computers can “really” think, refer, be conscious, and so on—have led to new conversations about how we should deal with the forms that many argue actually are implemented. These “AIs”, if they achieve “Superintelligence” (Nick Bostrom), could pose “existential risks” that lead to “Our Final Hour” (Martin Rees). And Stephen Hawking recently made international headlines when he noted “The development of full artificial intelligence could spell the end of the human race.”
But wait! Should we also ask what machines that think, or, “AIs”, might be thinking about? Do they want, do they expect civil rights? Do they have feelings? What kind of government (for us) would an AI choose? What kind of society would they want to structure for themselves? Or is “their” society “our” society? Will we, and the AIs, include each other within our respective circles of empathy?
The essays are now out in book form, and serve as a good quick-and-dirty tour of common ideas about smarter-than-human AI. The submissions, however, add up to 541 pages in book form, and MIRI’s focus on de novo AI makes us especially interested in the views of computer professionals. To make it easier to dive into the collection, I’ve collected a shorter list of links — the 32 argumentative essays written by computer scientists and software engineers.1 The resultant list includes three MIRI advisors (Omohundro, Russell, Tallinn) and one MIRI researcher (Yudkowsky).
I’ve excerpted passages from each of the essays below, focusing on discussions of AI motivations and outcomes. None of the excerpts is intended to distill the content of the entire essay, so you’re encouraged to read the full essay if an excerpt interests you.
- The exclusion of other groups from this list shouldn’t be taken to imply that this group is uniquely qualified to make predictions about AI. Psychology and neuroscience are highly relevant to this debate, as are disciplines that inform theoretical upper bounds on cognitive ability (e.g., mathematics and physics) and disciplines that investigate how technology is developed and used (e.g., economics and sociology). ↩
Today we release a new report by Katja Grace, “Leó Szilárd and the Danger of Nuclear Weapons: A Case Study in Risk Mitigation” (PDF, 72pp).
Leó Szilárd has been cited as an example of someone who predicted a highly disruptive technology years in advance — nuclear weapons — and successfully acted to reduce the risk. We conducted this investigation to check whether that basic story is true, and to determine whether we can take away any lessons from this episode that bear on highly advanced AI or other potentially disruptive technologies.
To prepare this report, Grace consulted several primary and secondary sources, and also conducted two interviews that are cited in the report and published here:
The basic conclusions of this report, which have not been separately vetted, are:
- Szilárd made several successful and important medium-term predictions — for example, that a nuclear chain reaction was possible, that it could produce a bomb thousands of times more powerful than existing bombs, and that such bombs could play a critical role in the ongoing conflict with Germany.
- Szilárd secretly patented the nuclear chain reaction in 1934, 11 years before the creation of the first nuclear weapon. It’s not clear whether Szilárd’s patent was intended to keep nuclear technology secret or bring it to the attention of the military. In any case, it did neither.
- Szilárd’s other secrecy efforts were more successful. Szilárd caused many sensitive results in nuclear science to be withheld from publication, and his efforts seems to have encouraged additional secrecy efforts. This effort largely ended when a French physicist, Frédéric Joliot-Curie, declined to suppress a paper on neutron emission rates in fission. Joliot-Curie’s publication caused multiple world powers to initiate nuclear weapons programs.
- All told, Szilárd’s efforts probably slowed the German nuclear project in expectation. This may not have made much difference, however, because the German program ended up being far behind the US program for a number of unrelated reasons.
- Szilárd and Einstein successfully alerted Roosevelt to the feasibility of nuclear weapons in 1939. This prompted the creation of the Advisory Committee on Uranium (ACU), but the ACU does not appear to have caused the later acceleration of US nuclear weapons development.
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