Post-fundraiser update: Donors rallied late last month to get us most of the way to our first fundraiser goal, but we ultimately fell short. This means that we’ll need to make up the remaining $160k gap over the next month if we’re going to move forward on our 2017 plans. We’re in a good position to expand our research staff and trial a number of potential hires, but only if we feel confident about our funding prospects over the next few years.
Since we don’t have an official end-of-the-year fundraiser planned this time around, we’ll be relying more on word-of-mouth to reach new donors. To help us with our expansion plans, donate at https://intelligence.org/donate/ — and spread the word!
News and links
We concluded our 2016 fundraiser eleven days ago. Progress was slow at first, but our donors came together in a big way in the final week, nearly doubling our final total. In the end, donors raised $589,316 over six weeks, making this our second-largest fundraiser to date. I’m heartened by this show of support, and extremely grateful to the 247 distinct donors who contributed.
We made substantial progress toward our immediate funding goals, but ultimately fell short of our $750,000 target by about $160k. We have a number of hypotheses as to why, but our best guess at the moment is that we missed our target because more donors than expected are waiting until the end of the year to decide whether (and how much) to give.
We were experimenting this year with running just one fundraiser in the fall (replacing the summer and winter fundraisers we’ve run in years past) and spending less time over the year on fundraising. Our fundraiser ended up looking more like recent summer funding drives, however. This suggests that either many donors are waiting to give in November and December, or we’re seeing a significant decline in donor support:
Looking at our donor database, preliminary data weakly suggests that many traditionally-winter donors are holding off, but it’s still hard to say.
This dip in donations so far is offset by the Open Philanthropy Project’s generous $500k grant, which raises our overall 2016 revenue from $1.23M to $1.73M. However, $1.73M would still not be enough to cover our 2016 expenses, much less our expenses for the coming year:
(2016 and 2017 expenses are projected, and our 2016 revenue is as of November 11.)
To a first approximation, this level of support means that we can continue to move forward without scaling back our plans too much, but only if donors come together to fill what’s left of our $160k gap as the year draws to a close:
In practical terms, closing this gap will mean that we can likely trial more researchers over the coming year, spend less senior staff time on raising funds, and take on more ambitious outreach and researcher-pipeline projects. E.g., an additional expected $75k / year would likely cause us to trial one extra researcher over the next 18 months (maxing out at 3-5 trials).
Currently, we’re in a situation where we have a number of potential researchers that we would like to give a 3-month trial, and we lack the funding to trial all of them. If we don’t close the gap this winter, then it’s also likely that we’ll need to move significantly more slowly on hiring and trialing new researchers going forward.
Our main priority in fundraisers is generally to secure stable, long-term flows of funding to pay for researcher salaries — “stable” not necessarily at the level of individual donors, but at least at the level of the donor community at large. If we make up our shortfall in November and December, then this will suggest that we shouldn’t expect big year-to-year fluctuations in support, and therefore we can fairly quickly convert marginal donations into AI safety researchers. If we don’t make up our shortfall soon, then this will suggest that we should be generally more prepared for surprises, which will require building up a bigger runway before growing the team very much.
Although we aren’t officially running a fundraiser, we still have quite a bit of ground to cover, and we’ll need support from a lot of new and old donors alike to get the rest of the way to our $750k target. Visit intelligence.org/donate to donate toward this goal, and do spread the word to people who may be interested in supporting our work.
You have my gratitude, again, for helping us get this far. It isn’t clear yet whether we’re out of the woods, but we’re now in a position where success in our 2016 fundraising is definitely a realistic option, provided that we put some work into it over the next two months. Thank you.
In May, the White House Office of Science and Technology Policy (OSTP) announced “a new series of workshops and an interagency working group to learn more about the benefits and risks of artificial intelligence.” They hosted a June Workshop on Safety and Control for AI (videos), along with three other workshops, and issued a general request for information on AI (see MIRI’s primary submission here).
The OSTP has now released a report summarizing its conclusions, “Preparing for the Future of Artificial Intelligence,” and the result is very promising. The OSTP acknowledges the ongoing discussion about AI risk, and recommends “investing in research on longer-term capabilities and how their challenges might be managed”:
General AI (sometimes called Artificial General Intelligence, or AGI) refers to a notional future AI system that exhibits apparently intelligent behavior at least as advanced as a person across the full range of cognitive tasks. A broad chasm seems to separate today’s Narrow AI from the much more difficult challenge of General AI. Attempts to reach General AI by expanding Narrow AI solutions have made little headway over many decades of research. The current consensus of the private-sector expert community, with which the NSTC Committee on Technology concurs, is that General AI will not be achieved for at least decades.14
People have long speculated on the implications of computers becoming more intelligent than humans. Some predict that a sufficiently intelligent AI could be tasked with developing even better, more intelligent systems, and that these in turn could be used to create systems with yet greater intelligence, and so on, leading in principle to an “intelligence explosion” or “singularity” in which machines quickly race far ahead of humans in intelligence.15
In a dystopian vision of this process, these super-intelligent machines would exceed the ability of humanity to understand or control. If computers could exert control over many critical systems, the result could be havoc, with humans no longer in control of their destiny at best and extinct at worst. This scenario has long been the subject of science fiction stories, and recent pronouncements from some influential industry leaders have highlighted these fears.
A more positive view of the future held by many researchers sees instead the development of intelligent systems that work well as helpers, assistants, trainers, and teammates of humans, and are designed to operate safely and ethically.
The NSTC Committee on Technology’s assessment is that long-term concerns about super-intelligent General AI should have little impact on current policy. The policies the Federal Government should adopt in the near-to-medium term if these fears are justified are almost exactly the same policies the Federal Government should adopt if they are not justified. The best way to build capacity for addressing the longer-term speculative risks is to attack the less extreme risks already seen today, such as current security, privacy, and safety risks, while investing in research on longer-term capabilities and how their challenges might be managed. Additionally, as research and applications in the field continue to mature, practitioners of AI in government and business should approach advances with appropriate consideration of the long-term societal and ethical questions – in additional to just the technical questions – that such advances portend. Although prudence dictates some attention to the possibility that harmful superintelligence might someday become possible, these concerns should not be the main driver of public policy for AI.
Later, the report discusses “methods for monitoring and forecasting AI developments”:
One potentially useful line of research is to survey expert judgments over time. As one example, a survey of AI researchers found that 80 percent of respondents believed that human-level General AI will eventually be achieved, and half believed it is at least 50 percent likely to be achieved by the year 2040. Most respondents also believed that General AI will eventually surpass humans in general intelligence.50 While these particular predictions are highly uncertain, as discussed above, such surveys of expert judgment are useful, especially when they are repeated frequently enough to measure changes in judgment over time. One way to elicit frequent judgments is to run “forecasting tournaments” such as prediction markets, in which participants have financial incentives to make accurate predictions.51 Other research has found that technology developments can often be accurately predicted by analyzing trends in publication and patent data52. […]
When asked during the outreach workshops and meetings how government could recognize milestones of progress in the field, especially those that indicate the arrival of General AI may be approaching, researchers tended to give three distinct but related types of answers:
1. Success at broader, less structured tasks: In this view, the transition from present Narrow AI to an eventual General AI will occur by gradually broadening the capabilities of Narrow AI systems so that a single system can cover a wider range of less structured tasks. An example milestone in this area would be a housecleaning robot that is as capable as a person at the full range of routine housecleaning tasks.
2. Unification of different “styles” of AI methods: In this view, AI currently relies on a set of separate methods or approaches, each useful for different types of applications. The path to General AI would involve a progressive unification of these methods. A milestone would involve finding a single method that is able to address a larger domain of applications that previously required multiple methods.
3. Solving specific technical challenges, such as transfer learning: In this view, the path to General AI does not lie in progressive broadening of scope, nor in unification of existing methods, but in progress on specific technical grand challenges, opening up new ways forward. The most commonly cited challenge is transfer learning, which has the goal of creating a machine learning algorithm whose result can be broadly applied (or transferred) to a range of new applications.
The report also discusses the open problems outlined in “Concrete Problems in AI Safety” and cites the MIRI paper “The Errors, Insights and Lessons of Famous AI Predictions – and What They Mean for the Future.”
In related news, Barack Obama recently answered some questions about AI risk and Nick Bostrom’s Superintelligence in a Wired interview. After saying that “we’re still a reasonably long way away” from general AI (video) and that his directive to his national security team is to worry more about near-term security concerns (video), Obama adds:
Now, I think, as a precaution — and all of us have spoken to folks like Elon Musk who are concerned about the superintelligent machine — there’s some prudence in thinking about benchmarks that would indicate some general intelligence developing on the horizon. And if we can see that coming, over the course of three decades, five decades, whatever the latest estimates are — if ever, because there are also arguments that this thing’s a lot more complicated than people make it out to be — then future generations, or our kids, or our grandkids, are going to be able to see it coming and figure it out.
Nate, Malo, Jessica, Tsvi, and I will be answering questions tomorrow at the Effective Altruism Forum. If you’ve been curious about anything related to our research, plans, or general thoughts, you’re invited to submit your own questions in the comments below or at Ask MIRI Anything.
We’ve also posted a more detailed version of our fundraiser overview and case for MIRI at the EA Forum.
In other news, we have a new talk out with an overview of “Logical Induction,” our recent paper presenting (as Critch puts it) “a financial solution to the computer science problem of metamathematics”:
This version of the talk goes into more technical detail than our previous talk on logical induction.
Our big announcement this month is our paper “Logical Induction,” introducing an algorithm that learns to assign reasonable probabilities to mathematical, empirical, and self-referential claims in a way that outpaces deduction. MIRI’s 2016 fundraiser is also live, and runs through the end of October.
News and links
We’ve uploaded the final set of videos from our recent Colloquium Series on Robust and Beneficial AI (CSRBAI) at the MIRI office, co-hosted with the Future of Humanity Institute. A full list of CSRBAI talks with public video or slides:
- Stuart Russell (UC Berkeley) — AI: The Story So Far (slides)
- Alan Fern (Oregon State University) — Toward Recognizing and Explaining Uncertainty (slides 1, slides 2)
- Francesca Rossi (IBM Research) — Moral Preferences (slides)
- Tom Dietterich (Oregon State University) — Issues Concerning AI Transparency (slides)
- Stefano Ermon (Stanford) — Probabilistic Inference and Accuracy Guarantees (slides)
- Paul Christiano (UC Berkeley) — Training an Aligned Reinforcement Learning Agent
- Jim Babcock — The AGI Containment Problem (slides)
- Bart Selman (Cornell) — Non-Human Intelligence (slides)
- Jessica Taylor (MIRI) — Alignment for Advanced Machine Learning Systems
- Dylan Hadfield-Menell (UC Berkeley) — The Off-Switch: Designing Corrigible, yet Functional, Artificial Agents (slides)
- Bas Steunebrink (IDSIA) — About Understanding, Meaning, and Values (slides)
- Jan Leike (Future of Humanity Institute) — General Reinforcement Learning (slides)
- Tom Everitt (Australian National University) — Avoiding Wireheading with Value Reinforcement Learning (slides)
- Michael Wellman (University of Michigan) — Autonomous Agents in Financial Markets: Implications and Risks (slides)
- Stefano Albrecht (UT Austin) — Learning to Distinguish Between Belief and Truth (slides)
- Stuart Armstrong (Future of Humanity Institute) — Reduced Impact AI and Other Alternatives to Friendliness (slides)
- Andrew Critch (MIRI) — Robust Cooperation of Bounded Agents
Our 2016 fundraiser is underway! Unlike in past years, we’ll only be running one fundraiser in 2016, from Sep. 16 to Oct. 31. Our progress so far (updated live):
Employer matching and pledges to give later this year also count towards the total. Click here to learn more.
MIRI is a nonprofit research group based in Berkeley, California. We do foundational research in mathematics and computer science that’s aimed at ensuring that smarter-than-human AI systems have a positive impact on the world.
2016 has been a big year for MIRI, and for the wider field of AI alignment research. Our 2016 strategic update in early August reviewed a number of recent developments:
- A group of researchers headed by Chris Olah of Google Brain and Dario Amodei of OpenAI published “Concrete problems in AI safety,” a new set of research directions that are likely to bear both on near-term and long-term safety issues.
- Dylan Hadfield-Menell, Anca Dragan, Pieter Abbeel, and Stuart Russell published a new value learning framework, “Cooperative inverse reinforcement learning,” with implications for corrigibility.
- Laurent Orseau of Google DeepMind and Stuart Armstrong of the Future of Humanity Institute received positive attention from news outlets and from Alphabet executive chairman Eric Schmidt for their new paper “Safely interruptible agents,” partly supported by MIRI.
- MIRI ran a three-week AI safety and robustness colloquium and workshop series, with speakers including Stuart Russell, Tom Dietterich, Francesca Rossi, and Bart Selman.
- We received a generous $300,000 donation and expanded our research and ops teams.
- We started work on a new research agenda, “Alignment for advanced machine learning systems.” This agenda will be occupying about half of our time going forward, with the other half focusing on our agent foundations agenda.
We also published new results in decision theory and logical uncertainty, including “Parametric bounded Löb’s theorem and robust cooperation of bounded agents” and “A formal solution to the grain of truth problem.” For a survey of our research progress and other updates from last year, see our 2015 review.
In the last three weeks, there have been three more major developments:
- We released a new paper, “Logical induction,” describing a method for learning to assign reasonable probabilities to mathematical conjectures and computational facts in a way that outpaces deduction.
- The Open Philanthropy Project awarded MIRI a one-year $500,000 grant to scale up our research program, with a strong chance of renewal next year.
- The Open Philanthropy Project is supporting the launch of the new UC Berkeley Center for Human-Compatible AI, headed by Stuart Russell.
Things have been moving fast over the last nine months. If we can replicate last year’s fundraising successes, we’ll be in an excellent position to move forward on our plans to grow our team and scale our research activities.
Read more »
MIRI is releasing a paper introducing a new model of deductively limited reasoning: “Logical induction,” authored by Scott Garrabrant, Tsvi Benson-Tilsen, Andrew Critch, myself, and Jessica Taylor. Readers may wish to start with the abridged version.
Consider a setting where a reasoner is observing a deductive process (such as a community of mathematicians and computer programmers) and waiting for proofs of various logical claims (such as the abc conjecture, or “this computer program has a bug in it”), while making guesses about which claims will turn out to be true. Roughly speaking, our paper presents a computable (though inefficient) algorithm that outpaces deduction, assigning high subjective probabilities to provable conjectures and low probabilities to disprovable conjectures long before the proofs can be produced.
This algorithm has a large number of nice theoretical properties. Still speaking roughly, the algorithm learns to assign probabilities to sentences in ways that respect any logical or statistical pattern that can be described in polynomial time. Additionally, it learns to reason well about its own beliefs and trust its future beliefs while avoiding paradox. Quoting from the abstract:
These properties and many others all follow from a single logical induction criterion, which is motivated by a series of stock trading analogies. Roughly speaking, each logical sentence φ is associated with a stock that is worth $1 per share if φ is true and nothing otherwise, and we interpret the belief-state of a logically uncertain reasoner as a set of market prices, where ℙn(φ)=50% means that on day n, shares of φ may be bought or sold from the reasoner for 50¢. The logical induction criterion says (very roughly) that there should not be any polynomial-time computable trading strategy with finite risk tolerance that earns unbounded profits in that market over time.