MIRI has a new COO: Malo Bourgon

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Malo BourgonI’m happy to announce that Malo Bourgon, formerly a program management analyst at MIRI, has taken on a new leadership role as our chief operating officer.

As MIRI’s second-in-command, Malo will be taking over a lot of the hands-on work of coordinating our day-to-day activities: supervising our ops team, planning events, managing our finances, and overseeing internal systems. He’ll also be assisting me in organizational strategy and outreach work.

Prior to joining MIRI, Malo studied electrical, software, and systems engineering at the University of Guelph in Ontario. His professional interests included climate change mitigation, and during his master’s, he worked on a project to reduce waste through online detection of inefficient electric motors. Malo started working for us shortly after completing his master’s in early 2012, which makes him MIRI’s longest-standing team member next to Eliezer Yudkowsky.

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Announcing a new colloquium series and fellows program

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The Machine Intelligence Research Institute is accepting applicants to two summer programs: a three-week AI robustness and reliability colloquium series (co-run with the Oxford Future of Humanity Institute), and a two-week fellows program focused on helping new researchers contribute to MIRI’s technical agenda (co-run with the Center for Applied Rationality).


The Colloquium Series on Robust and Beneficial AI (CSRBAI), running from May 27 to June 17, is a new gathering of top researchers in academia and industry to tackle the kinds of technical questions featured in the Future of Life Institute’s long-term AI research priorities report and project grants, including transparency, error-tolerance, and preference specification in software systems.

The goal of the event is to spark new conversations and collaborations between safety-conscious AI scientists with a variety of backgrounds and research interests. Attendees will be invited to give and attend talks at MIRI’s Berkeley, California offices during Wednesday/Thursday/Friday colloquia, to participate in hands-on Saturday/Sunday workshops, and to drop by for open discussion days:

 

 

Scheduled speakers include Stuart Russell (May 27), UC Berkeley Professor of Computer Science and co-author of Artificial Intelligence: A Modern Approach, Tom Dietterich (May 27), AAAI President and OSU Director of Intelligent Systems, and Bart Selman (June 3), Cornell Professor of Computer Science.

Apply here to attend any portion of the event, as well as to propose a talk or discussion topic:

 

Application Form

 


The 2016 MIRI Summer Fellows program, running from June 19 to July 3, doubles as a workshop for developing new problem-solving skills and mathematical intuitions, and a crash course on MIRI’s active research projects.

This is a smaller and more focused version of the Summer Fellows program we ran last year, which resulted in multiple new hires for us. As such, the program also functions as a high-intensity research retreat where MIRI staff and potential collaborators can get to know each other and work together on important open problems in AI. Apply here to attend the program:

 

Application Form

 


Both programs are free of charge, including free room and board for all MIRI Summer Fellows program participants, free lunches and dinners for CSRBAI participants, and additional partial accommodations and travel assistance for select attendees. For additional information, see the CSRBAI event page and the MIRI Summer Fellows event page.

Seeking Research Fellows in Type Theory and Machine Self-Reference

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The Machine Intelligence Research Institute (MIRI) is accepting applications for a full-time research fellow to develop theorem provers with self-referential capabilities, beginning by implementing a strongly typed language within that very language. The goal of this research project will be to help us understand autonomous systems that can prove theorems about systems with similar deductive capabilities. Applicants should have experience programming in functional programming languages, with a preference for languages with dependent types, such as Agda, Coq, or Lean.

MIRI is a mathematics and computer science research institute specializing in long-term AI safety and robustness work. Our offices are in Berkeley, California, near the UC Berkeley campus.

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March 2016 Newsletter

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Research updates

General updates

  • MIRI and other Future of Life Institute (FLI) grantees participated in a AAAI workshop on AI safety this month.
  • MIRI researcher Eliezer Yudkowsky discusses Ray Kurzweil, the Bayesian brain hypothesis, and an eclectic mix of other topics in a new interview.
  • Alexei Andreev and Yudkowsky are seeking investors for Arbital, a new technology for explaining difficult topics in economics, mathematics, computer science, and other disciplines. As a demo, Yudkowsky has written a new and improved guide to Bayes’s Rule.

News and links


John Horgan interviews Eliezer Yudkowsky

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Eliezer Yudkowsky Scientific American writer John Horgan recently interviewed MIRI’s senior researcher and co-founder, Eliezer Yudkowsky. The email interview touched on a wide range of topics, from politics and religion to existential risk and Bayesian models of rationality.

Although Eliezer isn’t speaking in an official capacity in the interview, a number of the questions discussed are likely to be interesting to people who follow MIRI’s work. We’ve reproduced the full interview below.


John Horgan: When someone at a party asks what you do, what do you tell her?


Eliezer Yudkowsky: Depending on the venue: “I’m a decision theorist”, or “I’m a cofounder of the Machine Intelligence Research Institute”, or if it wasn’t that kind of party, I’d talk about my fiction.


John: What’s your favorite AI film and why?


Eliezer: AI in film is universally awful. Ex Machina is as close to being an exception to this rule as it is realistic to ask.


John: Is college overrated?


Eliezer: It’d be very surprising if college were underrated, given the social desirability bias of endorsing college. So far as I know, there’s no reason to disbelieve the economists who say that college has mostly become a positional good, and that previous efforts to increase the volume of student loans just increased the cost of college and the burden of graduate debt.


John: Why do you write fiction?


Eliezer: To paraphrase Wondermark, “Well, first I tried not making it, but then that didn’t work.”

Beyond that, nonfiction conveys knowledge and fiction conveys experience. If you want to understand a proof of Bayes’s Rule, I can use diagrams. If I want you to feel what it is to use Bayesian reasoning, I have to write a story in which some character is doing that.

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New paper: “Defining human values for value learners”

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Defining ValuesMIRI Research Associate Kaj Sotala recently presented a new paper, “Defining Human Values for Value Learners,” at the AAAI-16 AI, Society and Ethics workshop.

The abstract reads:

Hypothetical “value learning” AIs learn human values and then try to act according to those values. The design of such AIs, however, is hampered by the fact that there exists no satisfactory definition of what exactly human values are. After arguing that the standard concept of preference is insufficient as a definition, I draw on reinforcement learning theory, emotion research, and moral psychology to offer an alternative definition. In this definition, human values are conceptualized as mental representations that encode the brain’s value function (in the reinforcement learning sense) by being imbued with a context-sensitive affective gloss. I finish with a discussion of the implications that this hypothesis has on the design of value learners.

Economic treatments of agency standardly assume that preferences encode some consistent ordering over world-states revealed in agents’ choices. Real-world preferences, however, have structure that is not always captured in economic models. A person can have conflicting preferences about whether to study for an exam, for example, and the choice they end up making may depend on complex, context-sensitive psychological dynamics, rather than on a simple comparison of two numbers representing how much one wants to study or not study.

Sotala argues that our preferences are better understood in terms of evolutionary theory and reinforcement learning. Humans evolved to pursue activities that are likely to lead to certain outcomes — outcomes that tended to improve our ancestors’ fitness. We prefer those outcomes, even if they no longer actually maximize fitness; and we also prefer events that we have learned tend to produce such outcomes.

Affect and emotion, on Sotala’s account, psychologically mediate our preferences. We enjoy and desire states that are highly rewarding in our evolved reward function. Over time, we also learn to enjoy and desire states that seem likely to lead to high-reward states. On this view, our preferences function to group together events that lead on expectation to similarly rewarding outcomes for similar reasons; and over our lifetimes we come to inherently value states that lead to high reward, instead of just valuing such states instrumentally. Rather than directly mapping onto our rewards, our preferences map onto our expectation of rewards.

Sotala proposes that value learning systems informed by this model of human psychology could more reliably reconstruct human values. On this model, for example, we can expect human preferences to change as we find new ways to move toward high-reward states. New experiences can change which states my emotions categorize as “likely to lead to reward,” and they can thereby modify which states I enjoy and desire. Value learning systems that take these facts about humans’ psychological dynamics into account may be better equipped to take our likely future preferences into account, rather than optimizing for our current preferences alone.

February 2016 Newsletter

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Research updates

General updates

  • Fundraiser and grant successes: MIRI will be working with AI pioneer Stuart Russell and a to-be-determined postdoctoral researcher on the problem of corrigibility, thanks to a $75,000 grant by the Center for Long-Term Cybersecurity.

News and links


End-of-the-year fundraiser and grant successes

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Our winter fundraising drive has concluded. Thank you all for your support!

Through the month of December, 175 distinct donors gave a total of $351,298. Between this fundraiser and our summer fundraiser, which brought in $630k, we’ve seen a surge in our donor base; our previous fundraisers over the past five years had brought in on average $250k (in the winter) and $340k (in the summer). We additionally received about $170k in 2015 grants from the Future of Life Institute, and $150k in other donations.

In all, we’ve taken in about $1.3M in grants and contributions in 2015, up from our $1M average over the previous five years. As a result, we’re entering 2016 with a team of six full-time researchers and over a year of runway.

Our next big push will be to close the gap between our new budget and our annual revenue. In order to sustain our current growth plans — which are aimed at expanding to a team of approximately ten full-time researchers — we’ll need to begin consistently taking in close to $2M per year by mid-2017.

I believe this is an achievable goal, though it will take some work. It will be even more valuable if we can overshoot this goal and begin extending our runway and further expanding our research program. On the whole, I’m very excited to see what this new year brings.


In addition to our fundraiser successes, we’ve begun seeing new grant-winning success. In collaboration with Stuart Russell at UC Berkeley, we’ve won a $75,000 grant from the Berkeley Center for Long-Term Cybersecurity. The bulk of the grant will go to funding a new postdoctoral position at UC Berkeley under Stuart Russell. The postdoc will collaborate with Russell and MIRI Research Fellow Patrick LaVictoire on the problem of AI corrigibility, as described in the grant proposal:

Consider a system capable of building accurate models of itself and its human operators. If the system is constructed to pursue some set of goals that its operators later realize will lead to undesirable behavior, then the system will by default have incentives to deceive, manipulate, or resist its operators to prevent them from altering its current goals (as that would interfere with its ability to achieve its current goals). […]

We refer to agents that have no incentives to manipulate, resist, or deceive their operators as “corrigible agents,” using the term as defined by Soares et al. (2015). We propose to study different methods for designing agents that are in fact corrigible.

This postdoctoral position has not yet been filled. Expressions of interest can be emailed to alex@intelligence.org using the subject line “UC Berkeley expression of interest.”