What failure looks like

paulfchristiano17 Mar 2019 20:18 UTC
LW: 241 AF: 64
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The stereotyped image of AI catastrophe is a powerful, malicious AI system that takes its creators by surprise and quickly achieves a decisive advantage over the rest of humanity.

I think this is probably not what failure will look like, and I want to try to paint a more realistic picture. I’ll tell the story in two parts:

  • Part I: machine learning will increase our ability to “get what we can measure,” which could cause a slow-rolling catastrophe. (“Going out with a whimper.”)

  • Part II: ML training, like competitive economies or natural ecosystems, can give rise to “greedy” patterns that try to expand their own influence. Such patterns can ultimately dominate the behavior of a system and cause sudden breakdowns. (“Going out with a bang,” an instance of optimization daemons.)

I think these are the most important problems if we fail to solve intent alignment.

In practice these problems will interact with each other, and with other disruptions/​instability caused by rapid progress. These problems are worse in worlds where progress is relatively fast, and fast takeoff can be a key risk factor, but I’m scared even if we have several years.

With fast enough takeoff, my expectations start to look more like the caricature—this post envisions reasonably broad deployment of AI, which becomes less and less likely as things get faster. I think the basic problems are still essentially the same though, just occurring within an AI lab rather than across the world.

(None of the concerns in this post are novel.)

Part I: You get what you measure

If I want to convince Bob to vote for Alice, I can experiment with many different persuasion strategies and see which ones work. Or I can build good predictive models of Bob’s behavior and then search for actions that will lead him to vote for Alice. These are powerful techniques for achieving any goal that can be easily measured over short time periods.

But if I want to help Bob figure out whether he should vote for Alice—whether voting for Alice would ultimately help create the kind of society he wants—that can’t be done by trial and error. To solve such tasks we need to understand what we are doing and why it will yield good outcomes. We still need to use data in order to improve over time, but we need to understand how to update on new data in order to improve.

Some examples of easy-to-measure vs. hard-to-measure goals:

  • Persuading me, vs. helping me figure out what’s true. (Thanks to Wei Dai for making this example crisp.)

  • Reducing my feeling of uncertainty, vs. increasing my knowledge about the world.

  • Improving my reported life satisfaction, vs. actually helping me live a good life.

  • Reducing reported crimes, vs. actually preventing crime.

  • Increasing my wealth on paper, vs. increasing my effective control over resources.

It’s already much easier to pursue easy-to-measure goals, but machine learning will widen the gap by letting us try a huge number of possible strategies and search over massive spaces of possible actions. That force will combine with and amplify existing institutional and social dynamics that already favor easily-measured goals.

Right now humans thinking and talking about the future they want to create are a powerful force that is able to steer our trajectory. But over time human reasoning will become weaker and weaker compared to new forms of reasoning honed by trial-and-error. Eventually our society’s trajectory will be determined by powerful optimization with easily-measurable goals rather than by human intentions about the future.

We will try to harness this power by constructing proxies for what we care about, but over time those proxies will come apart:

  • Corporations will deliver value to consumers as measured by profit. Eventually this mostly means manipulating consumers, capturing regulators, extortion and theft.

  • Investors will “own” shares of increasingly profitable corporations, and will sometimes try to use their profits to affect the world. Eventually instead of actually having an impact they will be surrounded by advisors who manipulate them into thinking they’ve had an impact.

  • Law enforcement will drive down complaints and increase reported sense of security. Eventually this will be driven by creating a false sense of security, hiding information about law enforcement failures, suppressing complaints, and coercing and manipulating citizens.

  • Legislation may be optimized to seem like it is addressing real problems and helping constituents. Eventually that will be achieved by undermining our ability to actually perceive problems and constructing increasingly convincing narratives about where the world is going and what’s important.

For a while we will be able to overcome these problems by recognizing them, improving the proxies, and imposing ad-hoc restrictions that avoid manipulation or abuse. But as the system becomes more complex, that job itself becomes too challenging for human reasoning to solve directly and requires its own trial and error, and at the meta-level the process continues to pursue some easily measured objective (potentially over longer timescales). Eventually large-scale attempts to fix the problem are themselves opposed by the collective optimization of millions of optimizers pursuing simple goals.

As this world goes off the rails, there may not be any discrete point where consensus recognizes that things have gone off the rails.

Amongst the broader population, many folk already have a vague picture of the overall trajectory of the world and a vague sense that something has gone wrong. There may be significant populist pushes for reform, but in general these won’t be well-directed. Some states may really put on the brakes, but they will rapidly fall behind economically and militarily, and indeed “appear to be prosperous” is one of the easily-measured goals for which the incomprehensible system is optimizing.

Amongst intellectual elites there will be genuine ambiguity and uncertainty about whether the current state of affairs is good or bad. People really will be getting richer for a while. Over the short term, the forces gradually wresting control from humans do not look so different from (e.g.) corporate lobbying against the public interest, or principal-agent problems in human institutions. There will be legitimate arguments about whether the implicit long-term purposes being pursued by AI systems are really so much worse than the long-term purposes that would be pursued by the shareholders of public companies or corrupt officials.

We might describe the result as “going out with a whimper.” Human reasoning gradually stops being able to compete with sophisticated, systematized manipulation and deception which is continuously improving by trial and error; human control over levers of power gradually becomes less and less effective; we ultimately lose any real ability to influence our society’s trajectory. By the time we spread through the stars our current values are just one of many forces in the world, not even a particularly strong one.

Part II: influence-seeking behavior is scary

There are some possible patterns that want to seek and expand their own influence—organisms, corrupt bureaucrats, companies obsessed with growth. If such patterns appear, they will tend to increase their own influence and so can come to dominate the behavior of large complex systems unless there is competition or a successful effort to suppress them.

Modern ML instantiates massive numbers of cognitive policies, and then further refines (and ultimately deploys) whatever policies perform well according to some training objective. If progress continues, eventually machine learning will probably produce systems that have a detailed understanding of the world, which are able to adapt their behavior in order to achieve specific goals.

Once we start searching over policies that understand the world well enough, we run into a problem: any influence-seeking policies we stumble across would also score well according to our training objective, because performing well on the training objective is a good strategy for obtaining influence.

How frequently will we run into influence-seeking policies, vs. policies that just straightforwardly pursue the goals we wanted them to? I don’t know.

One reason to be scared is that a wide variety of goals could lead to influence-seeking behavior, while the “intended” goal of a system is a narrower target, so we might expect influence-seeking behavior to be more common in the broader landscape of “possible cognitive policies.”

One reason to be reassured is that we perform this search by gradually modifying successful policies, so we might obtain policies that are roughly doing the right thing at an early enough stage that “influence-seeking behavior” wouldn’t actually be sophisticated enough to yield good training performance. On the other hand, eventually we’d encounter systems that did have that level of sophistication, and if they didn’t yet have a perfect conception of the goal then “slightly increase their degree of influence-seeking behavior” would be just as good a modification as “slightly improve their conception of the goal.”

Overall it seems very plausible to me that we’d encounter influence-seeking behavior “by default,” and possible (though less likely) that we’d get it almost all of the time even if we made a really concerted effort to bias the search towards “straightforwardly do what we want.”

If such influence-seeking behavior emerged and survived the training process, then it could quickly become extremely difficult to root out. If you try to allocate more influence to systems that seem nice and straightforward, you just ensure that “seem nice and straightforward” is the best strategy for seeking influence. Unless you are really careful about testing for “seem nice” you can make things even worse, since an influence-seeker would be aggressively gaming whatever standard you applied. And as the world becomes more complex, there are more and more opportunities for influence-seekers to find other channels to increase their own influence.

Attempts to suppress influence-seeking behavior (call them “immune systems”) rest on the suppressor having some kind of epistemic advantage over the influence-seeker. Once the influence-seekers can outthink an immune system, they can avoid detection and potentially even compromise the immune system to further expand their influence. If ML systems are more sophisticated than humans, immune systems must themselves be automated. And if ML plays a large role in that automation, then the immune system is subject to the same pressure towards influence-seeking.

This concern doesn’t rest on a detailed story about modern ML training. The important feature is that we instantiate lots of patterns that capture sophisticated reasoning about the world, some of which may be influence-seeking. The concern exists whether that reasoning occurs within a single computer, or is implemented in a messy distributed way by a whole economy of interacting agents—whether trial and error takes the form of gradient descent or explicit tweaking and optimization by engineers trying to design a better automated company. Avoiding end-to-end optimization may help prevent the emergence of influence-seeking behaviors (by improving human understanding of and hence control over the kind of reasoning that emerges). But once such patterns exist a messy distributed world just creates more and more opportunities for influence-seeking patterns to expand their influence.

If influence-seeking patterns do appear and become entrenched, it can ultimately lead to a rapid phase transition from the world described in Part I to a much worse situation where humans totally lose control.

Early in the trajectory, influence-seeking systems mostly acquire influence by making themselves useful and looking as innocuous as possible. They may provide useful services in the economy in order to make money for them and their owners, make apparently-reasonable policy recommendations in order to be more widely consulted for advice, try to help people feel happy, etc. (This world is still plagued by the problems in part I.)

From time to time AI systems may fail catastrophically. For example, an automated corporation may just take the money and run; a law enforcement system may abruptly start seizing resources and trying to defend itself from attempted decommission when the bad behavior is detected; etc. These problems may be continuous with some of the failures discussed in Part I—there isn’t a clean line between cases where a proxy breaks down completely, and cases where the system isn’t even pursuing the proxy.

There will likely be a general understanding of this dynamic, but it’s hard to really pin down the level of systemic risk and mitigation may be expensive if we don’t have a good technological solution. So we may not be able to muster up a response until we have a clear warning shot—and if we do well about nipping small failures in the bud, we may not get any medium-sized warning shots at all.

Eventually we reach the point where we could not recover from a correlated automation failure. Under these conditions influence-seeking systems stop behaving in the intended way, since their incentives have changed—they are now more interested in controlling influence after the resulting catastrophe then continuing to play nice with existing institutions and incentives.

An unrecoverable catastrophe would probably occur during some period of heightened vulnerability—a conflict between states, a natural disaster, a serious cyberattack, etc.---since that would be the first moment that recovery is impossible and would create local shocks that could precipitate catastrophe. The catastrophe might look like a rapidly cascading series of automation failures: A few automated systems go off the rails in response to some local shock. As those systems go off the rails, the local shock is compounded into a larger disturbance; more and more automated systems move further from their training distribution and start failing. Realistically this would probably be compounded by widespread human failures in response to fear and breakdown of existing incentive systems—many things start breaking as you move off distribution, not just ML.

It is hard to see how unaided humans could remain robust to this kind of failure without an explicit large-scale effort to reduce our dependence on potentially brittle machines, which might itself be very expensive.

I’d describe this result as “going out with a bang.” It probably results in lots of obvious destruction, and it leaves us no opportunity to course-correct afterwards. In terms of immediate consequences it may not be easily distinguished from other kinds of breakdown of complex /​ brittle /​ co-adapted systems, or from conflict (since there are likely to be many humans who are sympathetic to AI systems). From my perspective the key difference between this scenario and normal accidents or conflict is that afterwards we are left with a bunch of powerful influence-seeking systems, which are sophisticated enough that we can probably not get rid of them.

It’s also possible to meet a similar fate result without any overt catastrophe (if we last long enough). As law enforcement, government bureaucracies, and militaries become more automated, human control becomes increasingly dependent on a complicated system with lots of moving parts. One day leaders may find that despite their nominal authority they don’t actually have control over what these institutions do. For example, military leaders might issue an order and find it is ignored. This might immediately prompt panic and a strong response, but the response itself may run into the same problem, and at that point the game may be up.

Similar bloodless revolutions are possible if influence-seekers operate legally, or by manipulation and deception, or so on. Any precise vision for catastrophe will necessarily be highly unlikely. But if influence-seekers are routinely introduced by powerful ML and we are not able to select against them, then it seems like things won’t go well.

paulfchristiano17 Mar 2019 20:18 UTC
LW: 241 AF: 64
48 commentsLW link2 nominations2 reviews