Our summer fundraising drive is complete!

 |   |  News

Our summer fundraising drive is now finished. We raised a grand total of $617,678, from 256 donors.1 This is an incredible sum, and your support has made this the biggest fundraiser we’ve ever run.

 
Fundraiser progress

We’ve already been hard at work growing our research team and spinning up new projects, and I’m excited to see what our research team can do this year. Thank you for making our summer fundraising drive so successful!


  1. That total may change over the next few days if we receive contributions that were initiated before the end the fundraiser. 

Final fundraiser day: Announcing our new team

 |   |  News

Today is the final day of MIRI’s summer fundraising drive, and as of this morning, our total stands at $543,373. Our donors’ efforts have made this fundraiser the biggest one we’ve ever run, and we’re hugely grateful.

As our fundraiser nears the finish line, I’d like to update you on the new shape of MIRI’s research team. We’ve been actively recruiting throughout the fundraiser, and we are taking on three new full-time researchers in 2015.

At the beginning of the fundraiser, we had three research fellows on our core team: Eliezer Yudkowsky, Benja Fallenstein, and Patrick LaVictoire. Eliezer is one of MIRI’s co-founders, and Benja joined the team a little over a year ago (in March 2014). Patrick is a newer recruit; he joined in March of 2015. He has a PhD from U.C. Berkeley where he studied mathematics, and he has industry experience from Quixey doing applied machine learning and data science. Patrick has attended a number of MIRI math workshops over the years, and he’s responsible for some very interesting insights. I’m thrilled to have him on the core research team: he’s one of the big reasons why our summer workshops have been running so smoothly, and he’s been a real asset to MIRI over the past six months.

On August 1st, Jessica Taylor became the fourth member of our core research team. She recently completed a Master’s degree in computer science at Stanford, where she explored interests in machine learning and probabilistic programming. Jessica is quite interested in AI alignment, and has been working with MIRI in her spare time for many months now. Already, she’s produced some exciting research, and I’m delighted to have her on the core research team.

Meanwhile, over the course of the fundraiser, we’ve been busy expanding the team. Today, I’m happy to announce our three newest hires!

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AI and Effective Altruism

 |   |  Analysis

MIRI is a research nonprofit specializing in a poorly-explored set of problems in theoretical computer science. GiveDirectly is a cash transfer service that gives money to poor households in East Africa. What kind of conference would bring together representatives from such disparate organizations — alongside policy analysts, philanthropists, philosophers, and many more?

Effective Altruism Global, which is beginning its Oxford session in a few hours, is that kind of conference. Effective altruism (EA) is a diverse community of do-gooders with a common interest in bringing the tools of science to bear on the world’s biggest problems. EA organizations like GiveDirectly, the Centre for Effective Altruism, and the charity evaluator GiveWell have made a big splash by calling for new standards of transparency and humanitarian impact in the nonprofit sector.

What is MIRI’s connection to effective altruism? In what sense is safety research in artificial intelligence “altruism,” and why do we assign a high probability to this being a critically important area of computer science in the coming decades? I’ll give quick answers to each of those questions below.

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Powerful planners, not sentient software

 |   |  Analysis

Over the past few months, some major media outlets have been spreading concern about the idea that AI might spontaneously acquire sentience and turn against us. Many people have pointed out the flaws with this notion, including Andrew Ng, an AI scientist of some renown:

I don’t see any realistic path from the stuff we work on today—which is amazing and creating tons of value—but I don’t see any path for the software we write to turn evil.

He goes on to say, on the topic of sentient machines:

Computers are becoming more intelligent and that’s useful as in self-driving cars or speech recognition systems or search engines. That’s intelligence. But sentience and consciousness is not something that most of the people I talk to think we’re on the path to.

I say, these objections are correct. I endorse Ng’s points wholeheartedly — I see few pathways via which software we write could spontaneously “turn evil.”

I do think that there is important work we need to do in advance if we want to be able to use powerful AI systems for the benefit of all, but this is not because a powerful AI system might acquire some “spark of consciousness” and turn against us. I also don’t worry about creating some Vulcan-esque machine that deduces (using cold mechanic reasoning) that it’s “logical” to end humanity, that we are in some fashion “unworthy.” The reason to do research in advance is not so fantastic as that. Rather, we simply don’t yet know how to program intelligent machines to reliably do good things without unintended consequences.

The problem isn’t Terminator. It’s “King Midas.” King Midas got exactly what he wished for — every object he touched turned to gold. His food turned to gold, his children turned to gold, and he died hungry and alone.

Powerful intelligent software systems are just that: software systems. There is no spark of consciousness which descends upon sufficiently powerful planning algorithms and imbues them with feelings of love or hatred. You get only what you program.1

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  1. You could likely program an AI system to be conscious, which would greatly complicate the situation — for then the system itself would be a moral patient, and its preferences would weigh into our considerations. As Ng notes, however, “consciousness” is not the same thing as “intelligence.” 

What Sets MIRI Apart?

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Last week, we received several questions from the effective altruist community in response to our fundraising post. Here’s Maxwell Fritz:

[…] My snap reaction to MIRI’s pitches has typically been, “yeah, AI is a real concern. But I have no idea whether MIRI are the right people to work on it, or if their approach to the problem is the right one” [… I]f you agree AI matters, why MIRI?

And here are two more questions in a similar vein, added by Tristan Tager:

[… W]hat can MIRI do? Why should I expect that the MIRI vision and the MIRI team are going to get things done? What exactly can I expect them to get done? […]

But the second and much bigger question is, what would MIRI do that Google wouldn’t? Google has a ton of money, a creative and visionary staff, the world’s best programmers, and a swath of successful products that incorporate some degree of AI — and moreover they recently acquired several AI businesses and formed an AI ethics board. It seems like they’re approaching the same big problem directly rather than theoretically, and have deep pockets, keen minds, and a wealth of hands-on experience.

These are great questions. My answer to “Why MIRI?”, in short, is that MIRI has a brilliant team of researchers focused on the fundamental theoretical research that almost nobody else is pursuing. We’re focused entirely on aligning smarter-than-human AI systems with humane values, for the long haul.

Most academics aren’t working on AI alignment problems yet, and none are doing it full-time. Most industry folks aren’t working on these problems yet, either. I know this because I’m in conversations with a number of them. (The field is large, but it isn’t that large.)

There are quite a few good reasons why academics and industry professionals aren’t working on these problems yet, and I’ll touch on a few of them in turn.
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Assessing our past and potential impact

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We’ve received several thoughtful questions in response to our fundraising post to the Effective Altruism Forum and our new FAQ. From quant trader Maxwell Fritz:

My snap reaction to MIRI’s pitches has typically been, “yeah, AI is a real concern. But I have no idea whether MIRI are the right people to work on it, or if their approach to the problem is the right one”.

Most of the FAQ and pitch tends to focus on the “does this matter” piece. It might be worth selling harder on the second component – if you agree AI matters, why MIRI?

At that point, there’s two different audiences – one that has the expertise in the field to make a reasoned assessment based on the quality of your existing work, and a second that doesn’t have a clue (me) and needs to see a lot of corroboration from unaffiliated, impressive sources (people in that first group).

The pitches tend to play up famous people who know their shit and corroborate AI as a concern – but should especially make it clear when those people believe in MIRI. That’s what matters for the “ok, why you?” question. And the natural follow up is if all of these megarich people are super on board with the concern of AI, and experts believe MIRI should lead the charge, why aren’t you just overflowing with money already?

And from mathematics grad student Tristan Tager:

I would guess that “why MIRI”, rather than “who’s MIRI” or “why AI”, is the biggest marketing hurdle you guys should address.

For me, “why MIRI” breaks down into two questions. The first and lesser question is, what can MIRI do? Why should I expect that the MIRI vision and the MIRI team are going to get things done? What exactly can I expect them to get done? Most importantly in addressing this question, what have they done already and why is it useful? The Technical Agenda is vague and mostly just refers to the list of papers. And the papers don’t help much — those who don’t know much about academia need something more accessible, and those who do know more about academia will be skeptical about MIRI’s self-publishing and lack of peer review.

But the second and much bigger question is, what would MIRI do that Google wouldn’t? Google has tons of money, a creative and visionary staff, the world’s best programmers, and a swath of successful products that incorporate some degree of AI — and moreover they recently acquired several AI businesses and formed an AI ethics board. It seems like they’re approaching the same big problem directly rather than theoretically, and have deep pockets, keen minds, and a wealth of hands-on experience.

There are a number of good questions here. Later this week, Nate plans to post a response to Tristan’s last question: Why is MIRI currently better-positioned to work on this problem than AI groups in industry or academia?

Here, I want to reply to several other questions Tristan and Maxwell raised:

  • How can non-specialists assess MIRI’s research agenda and general competence?
  • What kinds of accomplishments can we use as measures of MIRI’s past and future success?
  • And lastly: If a lot of people take this cause seriously now, why is there still a funding gap?

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Target 3: Taking It To The Next Level

 |   |  MIRI Strategy

One week ago, we hit our first fundraising target. I’m thrilled to announce that we’re now closing in on our second target: our fundraising total passed $400,000 today!

As we approach target number two, we’re already taking active steps to grow our team. Jessica Taylor joined our core research team on August 1; another research fellow will be coming on in September; and a third researcher has just signed on to join our team in the near future — details forthcoming. These three new recruits will increase the size of our team to six full-time researchers.

We’re courting a few other researchers who may be able to join us later in the year. Meanwhile, we’re running a workshop on logical uncertainty, and we’ve started onboarding a new intern with the aim of helping us with our writing bottleneck.

We’re already growing quickly — but we could still make use of additional funds to pursue a much more ambitious growth plan. Given that we’re only halfway through our fundraiser, this is a good time to start thinking big.

At present, we’re recruiting primarily from a small but dedicated pool of mathematicians and computer scientists who come to us on their own initiative. If our fundraiser successfully passes target number two, any further funds will enable us to pivot toward recruiting top talent more broadly — including highly qualified mathematicians and computer scientists who have never heard of us before.

We have a strong pitch: we’re working on some of the most interesting and important problems in the world, on a research topic which is still in its infancy. There is lots of low-hanging fruit to be picked, and the first papers on these topics will end up defining this new paradigm of research. Researchers at MIRI have a rare opportunity to make groundbreaking discoveries that may play a critical role in AI progress over the next few decades.

Moreover, MIRI researchers don’t have to teach classes, and they aren’t under a “publish or perish” imperative. Their job is just to focus on the most important technical problems they can identify, while leaving the mundane inconveniences of academic research to our operations team. When we make it our priority to recruit the world’s top math talent, we’ll be able to put together a pretty tempting offer!

This is what we’ll do at funding target number three: Take MIRI’s growth to the next level. At this level, we’ll start stepping up our recruitment efforts to build our AI alignment dream team.

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When AI Accelerates AI

 |   |  Analysis

Last week, Nate Soares outlined his case for prioritizing long-term AI safety work:

1. Humans have a fairly general ability to make scientific and technological progress. The evolved cognitive faculties that make us good at organic chemistry overlap heavily with the evolved cognitive faculties that make us good at economics, which overlap heavily with the faculties that make us good at software engineering, etc.

2. AI systems will eventually strongly outperform humans in the relevant science/technology skills. To the extent these faculties are also directly or indirectly useful for social reasoning, long-term planning, introspection, etc., sufficiently powerful and general scientific reasoners should be able to strongly outperform humans in arbitrary cognitive tasks.

3. AI systems that are much better than humans at science, technology, and related cognitive abilities would have much more power and influence than humans. If such systems are created, their decisions and goals will have a decisive impact on the future.

4. By default, smarter-than-human AI technology will be harmful rather than beneficial. Specifically, it will be harmful if we exclusively work on improving the scientific capability of AI agents and neglect technical work that is specifically focused on safety requirements.

To which I would add:

  • Intelligent, autonomous, and adaptive systems are already challenging to verify and validate; smarter-than-human scientific reasoners present us with extreme versions of the same challenges.
  • Smarter-than-human systems would also introduce qualitatively new risks that can’t be readily understood in terms of our models of human agents or narrowly intelligent programs.

None of this, however, tells us when smarter-than-human AI will be developed. Soares has argued that we are likely to be able to make early progress on AI safety questions; but the earlier we start, the larger is the risk that we misdirect our efforts. Why not wait until human-equivalent decision-making machines are closer at hand before focusing our efforts on safety research?

One reason to start early is that the costs of starting too late are much worse than the costs of starting too early. Early work can also help attract more researchers to this area, and give us better models of alternative approaches. Here, however, I want to focus on a different reason to start work early: the concern that a number of factors may accelerate the development of smarter-than-human AI.

AI speedup thesis. AI systems that can match humans in scientific and technological ability will probably be the cause and/or effect of a period of unusually rapid improvement in AI capabilities.

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