When I read posts about AI alignment on LW / AF/ Arbital, I almost always find a particular bundle of assumptions taken for granted: 

  • An AGI has a single terminal goal[1].
  • The goal is a fixed part of the AI's structure.  The internal dynamics of the AI, if left to their own devices, will never modify the goal.
  • The "outermost loop" of the AI's internal dynamics is an optimization process aimed at the goal, or at least the AI behaves just as though this were true.
  • This "outermost loop" or "fixed-terminal-goal-directed wrapper" chooses which of the AI's specific capabilities to deploy at any given time, and how to deploy it[2].
  • The AI's capabilities will themselves involve optimization for sub-goals that are not the same as the goal, and they will optimize for them very powerfully (hence "capabilities").  But it is "not enough" that the AI merely be good at optimization-for-subgoals: it will also have a fixed-terminal-goal-directed wrapper.
    • So, the AI may be very good at playing chess, and when it is playing chess, it may be running an internal routine that optimizes for winning chess.  This routine, and not the terminal-goal-directed wrapper around it, explains the AI's strong chess performance.  ("Maximize paperclips" does not tell you how to win at chess.)
    • The AI may also be good at things that are much more general than chess, such as "planning," "devising proofs in arbitrary formal systems," "inferring human mental states," or "coming up with parsimonious hypotheses to explain observations."  All of these are capacities[3] to optimize for a particular subgoal that is not the AI's terminal goal.
    • Although these subgoal-directed capabilities, and not the fixed-terminal-goal-directed wrapper, will constitute the reason the AI does well at anything it does well at, the AI must still have a fixed-terminal-goal-directed wrapper around them and apart from them.
  • There is no way for the terminal goal to change through bottom-up feedback from anything inside the wrapper.  The hierarchy of control is strict and only goes one way.

My question: why assume all this?  Most pressingly, why assume that the terminal goal is fixed, with no internal dynamics capable of updating it?

I often see the rapid capability gains of humans over other apes cited as a prototype case for the rapid capability gains we expect in AGI.  But humans do not have this wrapper structure!  Our goals often change over time.  (And we often permit or even welcome this, whereas an optimizing wrapper would try to prevent its goal from changing.)

Having the wrapper structure was evidently not necessary for our rapid capability gains.  Nor do I see reason to think that our capabilities result from us being “more structured like this” than other apes.  (Or to think that we are “more structured like this” than other apes in this first place.)

Our capabilities seem more like the subgoal capabilities discussed above: general and powerful tools, which can be "plugged in" to many different (sub)goals, and which do not require the piloting of a wrapper with a fixed goal to "work" properly.

Why expect the "wrapper" structure with fixed goals to emerge from an outer optimization process?  Are there any relevant examples of this happening via natural selection, or via gradient descent?

There are many, many posts on LW / AF/ Arbital about "optimization," its relation to intelligence, whether we should view AGIs as "optimizers" and in what senses, etc.  I have not read all of it.  Most of it touches only lightly, if at all, on my question.  For example:

  • There has been much discussion over whether an AGI would inevitably have (close to) consistent preferences, or would self-modify itself to have closer-to-consistent preferences.  See e.g. here, here, here, here.  Every post I've read on this topic implicitly assumes that the preferences are fixed in time.
  • Mesa-optimizers have been discussed extensively.  The same bundle of assumptions is made about mesa-optimizers.
  • It has been argued that if you already have the fixed-terminal-goal-directed wrapper structure, then you will prefer to avoid outside influences that will modify your goal.  This is true, but does not explain why the structure would emerge in the first place.
  • There are arguments (e.g.) that we should heuristically imagine a superintelligence as a powerful optimizer, to get ourselves to predict that it will not do things we know are suboptimal.  These arguments tell us to imagine the AGI picking actions that are optimal for a goal iff it is currently optimizing for that goal.  They don't tell us when it will be optimizing for which goals.

EY's notion of "consequentialism" seems closely related to this set of assumptions.  But, I can't extract an answer from the writing I've read on that topic.

EY seems to attribute what I've called the powerful "subgoal capabilities" of humans/AGI to a property called "cross-domain consequentialism":

We can see one of the critical aspects of human intelligence as cross-domain consequentialism. Rather than only forecasting consequences within the boundaries of a narrow domain, we can trace chains of events that leap from one domain to another. Making a chess move wins a chess game that wins a chess tournament that wins prize money that can be used to rent a car that can drive to the supermarket to get milk. An Artificial General Intelligence that could learn many domains, and engage in consequentialist reasoning that leaped across those domains, would be a sufficiently advanced agent to be interesting from most perspectives on interestingness. It would start to be a consequentialist about the real world.

while defining "consequentialism" as the ability to do means-end reasoning with some preference ordering:

Whenever we reason that an agent which prefers outcome Y over Y' will therefore do X instead of X' we're implicitly assuming that the agent has the cognitive ability to do consequentialism at least about Xs and Ys. It does means-end reasoning; it selects means on the basis of their predicted ends plus a preference over ends.

But the ability to use this kind of reasoning, and do so across domains, does not imply that one's "outermost loop" looks like this kind of reasoning applied to the whole world at once.

I myself am a cross-domain consequentialist -- a human -- with very general capacities to reason and plan that I deploy across many different facets of my life.  But I'm not running an outermost loop with a fixed goal that pilots around all of my reasoning-and-planning activities.  Why can't AGI be like me?

EDIT to spell out the reason I care about the answer: agents with the "wrapper structure" are inevitably hard to align, in ways that agents without it might not be.  An AGI "like me" might be morally uncertain like I am, persuadable through dialogue like I am, etc.

It's very important to know what kind of AIs would or would not have the wrapper structure, because this makes the difference between "inevitable world-ending nightmare" and "we're not the dominant species anymore."  The latter would be pretty bad for us too, but there's a difference!

  1. ^

    Often people speak of the AI's "utility function" or "preference ordering" rather than its "goal."

    For my purposes here, these terms are more or less equivalent: it doesn't matter whether you think an AGI must have consistent preferences, only whether you think it must have fixed preferences.

  2. ^

    ...or at least the AI behaves just as though this were true.  I'll stop including this caveat after this.

  3. ^

    Or possibly one big capacity -- "general reasoning" or what have you -- which contains the others as special cases.  I'm not taking a position on how modular the capabilities will be.

New Answer
New Comment

1 Answers sorted by

The standard argument is as follows:

Imagine Mahatma Ghandi. He values non-violence above all other things. You offer him a pill, saying "Here, try my new 'turns you into a homicidal manic' pill." He replies "No thank-you - I don't want to kill people, thus I also don't want to become a homicidal maniac who will want to kill people."

If an AI has a utility function that it optimizes in order to tell it how to act, then, regardless of what that function is, it disagrees with all other (non-isomorphic) utility functions in at least some places, thus it regards them as inferior to itself -- so if it is offered the choice "Should I change from you to to this alternative utility function ?" it will always answer "no".

So this basic and widely modeled design for an AI is inherently dogmatic and non-corrigible, and will always seek to preserve its goal. So if you use this kind of AI, its goals are stable but non-corrigible, and (once it becomes powerful enough to stop you shutting it down) you get only one try at exactly aligning them. Humans are famously bad at writing reward functions, so this is unwise.

Note that most humans don't work like this - they are at least willing to consider updating their utility function to a better one. In fact, we even have a word for someone who has this particular mental failing: 'dogmatism'. This is because most humans are aware that their model of how the universe works is neither complete nor entirely accurate - as indeed any rational entity should be.

Reinforcement Learning machines also don't work this way -- they're trying to learn the utility function to use, so they update it often, and they don't ask the previous utility function if that was a good idea since its reply will always be 'no' so is useless input.

There are alternative designs, see for example the Human Compatible/CIRL/Value Learning approach suggested by Stuart Russell and others, which is simultaneously trying to find out what its utility function should be (where 'should' is defined as 'humans would want it to be, but sadly are not good enough at writing reward functions to be able to tell me') so doing Bayesian updates to it as it gathers more information about what humans actually want, and also optimizing its actions while internally modelling its uncertainty about the utility of possible actions as a probability distribution of possible utilities for an action (i.e. it can model situations like "I'm about ~95% convinced that this act will just produce the true-as-judged-by-humans utility level 'I fetched a human some coffee (+1)', but I'm uncertain, and there's also an ~5% chance I current misunderstand humans so badly that it might instead have a true utility level of  'the extinction of the human species (-10^25)', so I won't do it, and will consider spawning a subgoal of my 'become a better coffee fetcher' goal to further investigate this uncertainty, by some means far safer than just trying it and seeing what happens." Note that the utility probability distribution contains more information than just its mean would: it can both be updated in a more Bayesian way, and optimized over in a more cautious way (for example, it you were optimizing over O(20) possible actions, you should probably optimize against a score of "I'm ~95% confident that the utility is at least this", so roughly two sigma below the mean if your distribution is normal - which it may well not be - to avoid building an optimizer that mostly retrieves actions for which your error bars are wide. Similarly if you're optimizing over O(10,000) possible actions, you should probably optimize the 99.99%-confidence lower bounds on utility, and thus also consider some really unlikely ways in which you might be mistaken about what humans want.