August 2017 Newsletter
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Research updates
General updates
News and links
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July 2017 Newsletter
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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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Updates to the research team, and a major donation
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.
June 2017 Newsletter
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Research updates
General updates
News and links
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May 2017 Newsletter
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Research updates
General updates
News and links
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2017 Updates and Strategy
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
- AI research is becoming more visibly exciting and well-funded. This suggests that more top talent (in the next generation as well as the current generation) will probably turn their attention to AI.
- AGI is attracting more scholarly attention as an idea, and is the stated goal of top AI groups like DeepMind, OpenAI, and FAIR. In particular, many researchers seem more open to thinking about general intelligence now than they did a few years ago.
- Research groups associated with AGI are showing much clearer external signs of profitability.
- AI successes like AlphaGo indicate that it’s easier to outperform top humans in domains like Go (without any new conceptual breakthroughs) than might have been expected.2 This lowers our estimate for the number of significant conceptual breakthroughs needed to rival humans in other domains.
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.
- Note that this list is far from exhaustive. ↩
- Relatively general algorithms (plus copious compute) were able to surpass human performance on Go, going from incapable of winning against the worst human professionals in standard play to dominating the very best professionals in the space of a few months. The relevant development here wasn’t “AlphaGo represents a large conceptual advance over previously known techniques,” but rather “contemporary techniques run into surprisingly few obstacles when scaled to tasks as pattern-recognition-reliant and difficult (for humans) as professional Go”. ↩
- The publication of “Concrete Problems in AI Safety” last year, for example, caused us to reduce the time we were spending on broad-based outreach to the AI community at large in favor of spending more time building stronger collaborations with researchers we knew at OpenAI, Google Brain, DeepMind, and elsewhere. ↩
- Nate continues to set MIRI’s organizational strategy, and is responsible for the ideas in this post. ↩
Software Engineer Internship / Staff Openings
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.
About the Internship Program
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:
- Replicate recent machine learning papers, and implement variations.
- Learn about and implement machine learning tools (including results in the fields of deep learning, convex optimization, etc.).
- Run various coding experiments and projects, either independently or in small groups.
- Rapidly prototype, implement, and test AI alignment ideas related to machine learning (after demonstrating successes in the above points).
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.
The Ideal Candidate
Some qualities of the ideal candidate:
- Extensive breadth and depth of programming skills. Machine learning experience is not required, though it is a plus.
- Highly familiar with basic ideas related to AI alignment.
- Able to work independently with minimal supervision, and in team/group settings.
- Willing to accept a below-market rate. Since MIRI is a non-profit, we can’t compete with the Big Names in the Bay Area.
- Enthusiastic about the prospect of working at MIRI and helping advance the field of AI alignment.
- Not looking for a “generic” software engineering position.
Working at MIRI
We strive to make working at MIRI a rewarding experience.
- Modern Work Spaces — Many of us have adjustable standing desks with large external monitors. We consider workspace ergonomics important, and try to rig up work stations to be as comfortable as possible. Free snacks, drinks, and meals are also provided at our office.
- Flexible Hours — We don’t have strict office hours, and we don’t limit employees’ vacation days. Our goal is to make rapid progress on our research agenda, and we would prefer that staff take a day off than that they extend tasks to fill an extra day.
- Living in the Bay Area — MIRI’s office is located in downtown Berkeley, California. From our office, you’re a 30-second walk to the BART (Bay Area Rapid Transit), which can get you around the Bay Area; a 3-minute walk to UC Berkeley campus; and a 30-minute BART ride to downtown San Francisco.
EEO & Employment Eligibility
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.
Apply
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.
Ensuring smarter-than-human intelligence has a positive outcome
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
