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?

 |   |  Analysis

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

 |   |  Analysis

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? (Edit Feb. 17: Link here.)

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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August 2015 Newsletter

 |   |  Newsletters

Research updates

General updates

News and links

  • We’re at day three of the Effective Altruism Global conference! You can watch a selection of talks on the livestream.
  • Thousands sign an open letter by the Future of Life Institute advocating “a ban on offensive autonomous weapons beyond meaningful human control.”

A new MIRI FAQ, and other announcements

 |   |  News

MIRI is at Effective Altruism Global! A number of the talks can be watched online at the EA Global Livestream.

We have a new MIRI Frequently Asked Questions page, which we’ll be expanding as we continue getting new questions over the next four weeks. Questions covered so far include “Why is safety important for smarter-than-human AI?” and “Do researchers think AI is imminent?

We’ve also been updating other pages on our website. About MIRI now functions as a short introduction to our mission, and Get Involved has a new consolidated application form for people who want to collaborate with us on our research program.

Finally, an announcement: just two weeks into our six-week fundraiser, we have hit our first major fundraising target! We extend our thanks to the donors who got us here so quickly. Thanks to you, we now have the funds to expand our core research team to 6–8 people for the coming year.

New donations we receive at https://intelligence.org/donate will now go toward our second target: “Accelerated Growth.” If we hit this second target ($500k total), we will be able to expand to a ten-person core team and take on a number of important new projects. More details on our plans if we hit our first two fundraiser targets: Growing MIRI.

MIRI’s Approach

 |   |  Analysis

MIRI’s mission is “to ensure that the creation of smarter-than-human artificial intelligence has a positive impact.” How can we ensure any such thing? It’s a daunting task, especially given that we don’t have any smarter-than-human machines to work with at the moment. In the previous post I discussed four background claims that motivate our mission; in this post I will describe our approach to addressing the challenge.

This challenge is sizeable, and we can only tackle a portion of the problem. For this reason, we specialize. Our two biggest specializing assumptions are as follows:

We focus on scenarios where smarter-than-human machine intelligence is first created in de novo software systems (as opposed to, say, brain emulations).

This is in part because it seems difficult to get all the way to brain emulation before someone reverse-engineers the algorithms used by the brain and uses them in a software system, and in part because we expect that any highly reliable AI system will need to have at least some components built from the ground up for safety and transparency. Nevertheless, it is quite plausible that early superintelligent systems will not be human-designed software, and I strongly endorse research programs that focus on reducing risks along the other pathways.

We specialize almost entirely in technical research.

We select our researchers for their proficiency in mathematics and computer science, rather than forecasting expertise or political acumen. I stress that this is only one part of the puzzle: figuring out how to build the right system is useless if the right system does not in fact get built, and ensuring AI has a positive impact is not simply a technical problem. It is also a global coordination problem, in the face of short-term incentives to cut corners. Addressing these non-technical challenges is an important task that we do not focus on.

In short, MIRI does technical research to ensure that de novo AI software systems will have a positive impact. We do not further discriminate between different types of AI software systems, nor do we make strong claims about exactly how quickly we expect AI systems to attain superintelligence. Rather, our current approach is to select open problems using the following question:

What would we still be unable to solve, even if the challenge were far simpler?

For example, we might study AI alignment problems that we could not solve even if we had lots of computing power and very simple goals.

We then filter on problems that are (1) tractable, in the sense that we can do productive mathematical research on them today; (2) uncrowded, in the sense that the problems are not likely to be addressed during normal capabilities research; and (3) critical, in the sense that they could not be safely delegated to a machine unless we had first solved them ourselves. (Since the goal is to design intelligent machines, there are many technical problems that we can expect to eventually delegate to those machines. But it is difficult to trust an unreliable reasoner with the task of designing reliable reasoning!)

These three filters are usually uncontroversial. The controversial claim here is that the above question — “what would we be unable to solve, even if the challenge were simpler?” — is a generator of open technical problems for which solutions will help us design safer and more reliable AI software in the future, regardless of their architecture. The rest of this post is dedicated to justifying this claim, and describing the reasoning behind it.

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