October 2017 Newsletter

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“So far as I can presently estimate, now that we’ve had AlphaGo and a couple of other maybe/maybe-not shots across the bow, and seen a huge explosion of effort invested into machine learning and an enormous flood of papers, we are probably going to occupy our present epistemic state until very near the end.

“[…I]t’s hard to guess how many further insights are needed for AGI, or how long it will take to reach those insights. After the next breakthrough, we still won’t know how many more breakthroughs are needed, leaving us in pretty much the same epistemic state as before. […] You can either act despite that, or not act. Not act until it’s too late to help much, in the best case; not act at all until after it’s essentially over, in the average case.”

Read more in a new blog post by Eliezer Yudkowsky: “There’s No Fire Alarm for Artificial General Intelligence.” (Discussion on LessWrong 2.0, Hacker News.)

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There’s No Fire Alarm for Artificial General Intelligence

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What is the function of a fire alarm?

 

One might think that the function of a fire alarm is to provide you with important evidence about a fire existing, allowing you to change your policy accordingly and exit the building.

In the classic experiment by Latane and Darley in 1968, eight groups of three students each were asked to fill out a questionnaire in a room that shortly after began filling up with smoke. Five out of the eight groups didn’t react or report the smoke, even as it became dense enough to make them start coughing. Subsequent manipulations showed that a lone student will respond 75% of the time; while a student accompanied by two actors told to feign apathy will respond only 10% of the time. This and other experiments seemed to pin down that what’s happening is pluralistic ignorance. We don’t want to look panicky by being afraid of what isn’t an emergency, so we try to look calm while glancing out of the corners of our eyes to see how others are reacting, but of course they are also trying to look calm.

(I’ve read a number of replications and variations on this research, and the effect size is blatant. I would not expect this to be one of the results that dies to the replication crisis, and I haven’t yet heard about the replication crisis touching it. But we have to put a maybe-not marker on everything now.)

A fire alarm creates common knowledge, in the you-know-I-know sense, that there is a fire; after which it is socially safe to react. When the fire alarm goes off, you know that everyone else knows there is a fire, you know you won’t lose face if you proceed to exit the building.

The fire alarm doesn’t tell us with certainty that a fire is there. In fact, I can’t recall one time in my life when, exiting a building on a fire alarm, there was an actual fire. Really, a fire alarm is weaker evidence of fire than smoke coming from under a door.

But the fire alarm tells us that it’s socially okay to react to the fire. It promises us with certainty that we won’t be embarrassed if we now proceed to exit in an orderly fashion.

It seems to me that this is one of the cases where people have mistaken beliefs about what they believe, like when somebody loudly endorsing their city’s team to win the big game will back down as soon as asked to bet. They haven’t consciously distinguished the rewarding exhilaration of shouting that the team will win, from the feeling of anticipating the team will win.

When people look at the smoke coming from under the door, I think they think their uncertain wobbling feeling comes from not assigning the fire a high-enough probability of really being there, and that they’re reluctant to act for fear of wasting effort and time. If so, I think they’re interpreting their own feelings mistakenly. If that was so, they’d get the same wobbly feeling on hearing the fire alarm, or even more so, because fire alarms correlate to fire less than does smoke coming from under a door. The uncertain wobbling feeling comes from the worry that others believe differently, not the worry that the fire isn’t there. The reluctance to act is the reluctance to be seen looking foolish, not the reluctance to waste effort. That’s why the student alone in the room does something about the fire 75% of the time, and why people have no trouble reacting to the much weaker evidence presented by fire alarms.

 


 

It’s now and then proposed that we ought to start reacting later to the issues of Artificial General Intelligence (background here), because, it is said, we are so far away from it that it just isn’t possible to do productive work on it today.

(For direct argument about there being things doable today, see: Soares and Fallenstein (2014/2017); Amodei, Olah, Steinhardt, Christiano, Schulman, and Mané (2016); or Taylor, Yudkowsky, LaVictoire, and Critch (2016).)

(If none of those papers existed or if you were an AI researcher who’d read them but thought they were all garbage, and you wished you could work on alignment but knew of nothing you could do, the wise next step would be to sit down and spend two hours by the clock sincerely trying to think of possible approaches. Preferably without self-sabotage that makes sure you don’t come up with anything plausible; as might happen if, hypothetically speaking, you would actually find it much more comfortable to believe there was nothing you ought to be working on today, because e.g. then you could work on other things that interested you more.)

(But never mind.)

So if AGI seems far-ish away, and you think the conclusion licensed by this is that you can’t do any productive work on AGI alignment yet, then the implicit alternative strategy on offer is: Wait for some unspecified future event that tells us AGI is coming near; and then we’ll all know that it’s okay to start working on AGI alignment.

This seems to me to be wrong on a number of grounds. Here are some of them.

Read more »

September 2017 Newsletter

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Research updates

General updates

  • As part of his engineering internship at MIRI, Max Harms assisted in the construction and extension of RL-Teacher, an open-source tool for training AI systems with human feedback based on the “Deep RL from Human Preferences” OpenAI / DeepMind research collaboration. See OpenAI’s announcement.
  • MIRI COO Malo Bourgon participated in panel discussions on getting things done (video) and working in AI (video) at the Effective Altruism Global conference in San Francisco. AI Impacts researcher Katja Grace also spoke on AI safety (video). Other EAG talks on AI included Daniel Dewey’s (video) and Owen Cotton-Barratt’s (video), and a larger panel discussion (video).
  • Announcing two winners of the Intelligence in Literature prize: Laurence Raphael Brothers’ “Houseproud” and Shane Halbach’s “Human in the Loop”.
  • RAISE, a project to develop online AI alignment course material, is seeking volunteers. 

News and links

  • The Open Philanthropy Project is accepting applicants to an AI Fellows Program “to fully support a small group of the most promising PhD students in artificial intelligence and machine learning”. See also Open Phil’s partial list of key research topics in AI alignment.
  • Call for papers: AAAI and ACM are running a new Conference on AI, Ethics, and Society, with submissions due by the end of October.
  • DeepMind’s Viktoriya Krakovna argues for a portfolio approach to AI safety research.
  • Teaching AI Systems to Behave Themselves”: a solid article from the New York Times on the growing field of AI safety research. The Times also has an opening for an investigative reporter in AI.
  • UC Berkeley’s Center for Long-term Cybersecurity is hiring for several roles, including researcher, assistant to the director, and program manager.
  • Life 3.0: Max Tegmark releases a new book on the future of AI (podcast discussion).

New paper: “Incorrigibility in the CIRL Framework”

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Incorrigibility in the CIRL Framework

MIRI assistant research fellow Ryan Carey has a new paper out discussing situations where good performance in Cooperative Inverse Reinforcement Learning (CIRL) tasks fails to imply that software agents will assist or cooperate with programmers.

The paper, titled “Incorrigibility in the CIRL Framework,” lays out four scenarios in which CIRL violates the four conditions for corrigibility defined in Soares et al. (2015). Abstract:

A value learning system has incentives to follow shutdown instructions, assuming the shutdown instruction provides information (in the technical sense) about which actions lead to valuable outcomes. However, this assumption is not robust to model mis-specification (e.g., in the case of programmer errors). We demonstrate this by presenting some Supervised POMDP scenarios in which errors in the parameterized reward function remove the incentive to follow shutdown commands. These difficulties parallel those discussed by Soares et al. (2015) in their paper on corrigibility.

We argue that it is important to consider systems that follow shutdown commands under some weaker set of assumptions (e.g., that one small verified module is correctly implemented; as opposed to an entire prior probability distribution and/or parameterized reward function). We discuss some difficulties with simple ways to attempt to attain these sorts of guarantees in a value learning framework.

The paper is a response to a paper by Hadfield-Menell, Dragan, Abbeel, and Russell, “The Off-Switch Game.” Hadfield-Menell et al. show that an AI system will be more responsive to human inputs when it is uncertain about its reward function and thinks that its human operator has more information about this reward function. Carey shows that the CIRL framework can be used to formalize the problem of corrigibility, and that the known assurances for CIRL systems, given in “The Off-Switch Game”, rely on strong assumptions about having an error-free CIRL system. With less idealized assumptions, a value learning agent may have beliefs that cause it to evade redirection from the human.

[T]he purpose of a shutdown button is to shut the AI system down in the event that all other assurances failed, e.g., in the event that the AI system is ignoring (for one reason or another) the instructions of the operators. If the designers of [the AI system] R have programmed the system so perfectly that the prior and [reward function] R are completely free of bugs, then the theorems of Hadfield-Menell et al. (2017) do apply. In practice, this means that in order to be corrigible, it would be necessary to have an AI system that was uncertain about all things that could possibly matter. The problem is that performing Bayesian reasoning over all possible worlds and all possible value functions is quite intractable. Realistically, humans will likely have to use a large number of heuristics and approximations in order to implement the system’s belief system and updating rules. […]

Soares et al. (2015) seem to want a shutdown button that works as a mechanism of last resort, to shut an AI system down in cases where it has observed and refused a programmer suggestion (and the programmers believe that the system is malfunctioning). Clearly, some part of the system must be working correctly in order for us to expect the shutdown button to work at all. However, it seems undesirable for the working of the button to depend on there being zero critical errors in the specification of the system’s prior, the specification of the reward function, the way it categorizes different types of actions, and so on. Instead, it is desirable to develop a shutdown module that is small and simple, with code that could ideally be rigorously verified, and which ideally works to shut the system down even in the event of large programmer errors in the specification of the rest of the system.

In order to do this in a value learning framework, we require a value learning system that (i) is capable of having its actions overridden by a small verified module that watches for shutdown commands; (ii) has no incentive to remove, damage, or ignore the shutdown module; and (iii) has some small incentive to keep its shutdown module around; even under a broad range of cases where R, the prior, the set of available actions, etc. are misspecified.

Even if the utility function is learned, there is still a need for additional lines of defense against unintended failures. The hope is that this can be achieved by modularizing the AI system. For that purpose, we would need a model of an agent that will behave corrigibly in a way that is robust to misspecification of other system components.

 

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August 2017 Newsletter

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Research updates

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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

Updates to the research team, and a major donation

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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.


Read more »

June 2017 Newsletter

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Research updates

General updates

News and links