This post sketches two challenges to ARC's project around eliciting latent
knowledge that differ somewhat in kind from the challenges ARC is most
concerned about. They relate to the difficulty in distinguishing beliefs
from other representations.
Introduction
The problem of ELK, as outlined in ARC's technical
report,
is to figure out: given an AI trained to complete a task (the 'task AI'),
how can we design a second AI (the 'reporter AI') that can answer
questions about how the task AI understands the world. If we can know what
the task AI is thinking, we can better assess whether its decisions are
dangerous.
The central challenge identified by ARC to solving the problem of ELK
concerns the possibility that the reporter AI will not learn to answer
questions using what the task AI believes (e.g. answering honestly), but
instead answer questions using what a human observer might think is the
case on the basis of the available evidence (answering empathetically). We
train the reporter AI to answer based on the facts as we understand them
(and know the task AI to understand them) in relatively simple cases. We
hope that the reporter will answer questions in more complex cases based
on how the task AI understands the world, but it might instead answer
based on what it thinks a human would mistakenly believe. If the human
questioner is ignorant of some complex details that the task AI knows, the
reporter AI might choose to leave them out to tell a simpler story that
will satisfy the questioner. It is not obvious how to train an AI to
answer honestly rather than empathetically.
While this is a significant challenge, I don't think it is the only
challenge for ELK. In thinking through the problem, I became worried about
several others. I here describe two other challenges. Though they are
somewhat speculative, ARC aims for a solution to the problem of ELK that deals
with the worst case, and these challenges fall under that umbrella.
Even if we figure out how to make an honest rather than a empathetic
reporter, these challenges will remain.
1.) The Right Representation Challenge
We do not know how the task AI might encode the the things that it
believes, but what it knows about the world must somehow be reflected in
its neural activity. For simplicity, let's suppose that the task AI utilizes
explicit independent representational states that are encoded in its
network in something vaguely like the way sentences are written in a book. The
reporter AI would need to figure out where in the network these sentences
are recorded. It would then have to figure out how to interpret the
sentences and use them to provide answers to questions in English.
The reporter AI would also need to figure out which sentences recorded in
the task AI's network are really believed and which sentences are recorded
for other reasons. The task AI might produce a variety of representations
that it doesn't fully believe (i.e. doesn't straightforwardly rely on to
make decisions) for various purposes. If we just scan the network for
sentences, we may find sentences that it doesn't quite believe. These may
still correlate with what it does believe in training scenarios, and so
a reporter AI might learn to use them to answer questions. If the reporter
latches on to those sentences, then it might give misleading answers once
outside of training.
Consider the following possibilities:
The task AI makes predictions using the simulated mental states of various agents. In the central case of the report, the task is to prevent
a thief from stealing a diamond -- the task AI might keep track of what
the thief believes is happening, what a security guard thinks is
happening, what other would-be thieves might later believe happened, etc.
If our AI is sufficiently careful, it might keep track of what some
possible observers believe is happening. Perhaps it would simulate what
other superintelligent AIs with access to its sensors would think is
happening. Depending on its purview, these might be reasonable things for
it to consider. If there are some possible agents that happen to often
agree with the task AI (such as hypothetical superintelligent AIs with
access to its sensors), our reporter might have a hard time figuring out
which representations it should use to answer questions.
The task AI utilizes a parliamentarian architecture in which it's
opinions are distributed over multiple specialized sub-agents, such that
what it thinks is a function of those sub-agents. This has been a popular
model for the human brain, so it seems conceivable that an AI would work
this way. The sub-agents would have their own beliefs. Sometimes those
beliefs would be wrong, but some sub-agents might be usually correct, at
least in training. Consider how you would go about deciding what
the US Senate currently collectively believes. A good strategy may just be to ask
Joe Manchin. This strategy may stop working in 2023. Our reporter might similarly be inclined to identify one
parliamentarian and report its representations. Even if the reporter decides to
aggregate the representations of the relevant agents, it may have trouble figuring
out how to do this correctly. There may be many good but not quite right
approaches to aggregation. It isn't obvious that an AI well trained on
a simple data set will succeed in finding the right way to aggregate.
2.) The Creative Interpretation Challenge
Creative interpretation is the process through which we infer implicit
representations by means of explicit representations. We use creative
interpretation when reading fiction or watching movies. Some aspects of
the plot are not explicitly depicted, but are implied by what is depicted.
For instance, movies rarely explicitly say how much time passes between
scenes. We infer from contextual cues that it was a few hours, or a day,
or a few weeks.
If a reporter does utilize some explicit representations, it likely won't
record everything that it knows about the world explicitly. Some of its
beliefs must be inferred from what it does explicitly represent and how it
works with those representations. Some of its beliefs may depend on what
is missing from its explicit representations or may depend on patterns of
what it explicitly represents. A reporter is thus likely to need to do
some creative interpretation in order to suss out the task AI's full
beliefs.
Creative interpretation of a sort may be stretched even to cases where
there are no explicit representations. Some beliefs may be more concrete
or easier to access than others. In such cases, creative interpretation
might involve the inference of less easy to access facts from more easy to
access facts.
Given that a reporter needs to do some creative interpretation, the worry
is that it may do more than is appropriate. Compare the legitimate
creative interpretation of a novel by a careful reader with the kind of
thing a lazy student who didn't read a few chapters does when writing
a book report. They infer what must have happened from what they know.
A reporter which is good at interpreting some things may be bad at
interpreting others. If it is lazy, it may just try to figure out from
context what the task AI believes given a few snippets that are
particularly easy to decipher.
The challenge is to make sure that our reporter isn't inferring some of
the things it translates infelicitously. It is ok if our reporter is like
the average viewer of a movie. It is not ok if our reporter is like a lazy
English student bullshitting through a book report. We know what
distinguishes appropriate creative interpretation in books and movies. It
isn't obvious how to distinguish good cases of creative inference from bad
cases of inference in representational systems that we don't understand.
This post sketches two challenges to ARC's project around eliciting latent knowledge that differ somewhat in kind from the challenges ARC is most concerned about. They relate to the difficulty in distinguishing beliefs from other representations.
Introduction
The problem of ELK, as outlined in ARC's technical report, is to figure out: given an AI trained to complete a task (the 'task AI'), how can we design a second AI (the 'reporter AI') that can answer questions about how the task AI understands the world. If we can know what the task AI is thinking, we can better assess whether its decisions are dangerous.
The central challenge identified by ARC to solving the problem of ELK concerns the possibility that the reporter AI will not learn to answer questions using what the task AI believes (e.g. answering honestly), but instead answer questions using what a human observer might think is the case on the basis of the available evidence (answering empathetically). We train the reporter AI to answer based on the facts as we understand them (and know the task AI to understand them) in relatively simple cases. We hope that the reporter will answer questions in more complex cases based on how the task AI understands the world, but it might instead answer based on what it thinks a human would mistakenly believe. If the human questioner is ignorant of some complex details that the task AI knows, the reporter AI might choose to leave them out to tell a simpler story that will satisfy the questioner. It is not obvious how to train an AI to answer honestly rather than empathetically.
While this is a significant challenge, I don't think it is the only challenge for ELK. In thinking through the problem, I became worried about several others. I here describe two other challenges. Though they are somewhat speculative, ARC aims for a solution to the problem of ELK that deals with the worst case, and these challenges fall under that umbrella.
Even if we figure out how to make an honest rather than a empathetic reporter, these challenges will remain.
1.) The Right Representation Challenge
We do not know how the task AI might encode the the things that it believes, but what it knows about the world must somehow be reflected in its neural activity. For simplicity, let's suppose that the task AI utilizes explicit independent representational states that are encoded in its network in something vaguely like the way sentences are written in a book. The reporter AI would need to figure out where in the network these sentences are recorded. It would then have to figure out how to interpret the sentences and use them to provide answers to questions in English.
The reporter AI would also need to figure out which sentences recorded in the task AI's network are really believed and which sentences are recorded for other reasons. The task AI might produce a variety of representations that it doesn't fully believe (i.e. doesn't straightforwardly rely on to make decisions) for various purposes. If we just scan the network for sentences, we may find sentences that it doesn't quite believe. These may still correlate with what it does believe in training scenarios, and so a reporter AI might learn to use them to answer questions. If the reporter latches on to those sentences, then it might give misleading answers once outside of training.
Consider the following possibilities:
The task AI makes predictions using the simulated mental states of various agents. In the central case of the report, the task is to prevent a thief from stealing a diamond -- the task AI might keep track of what the thief believes is happening, what a security guard thinks is happening, what other would-be thieves might later believe happened, etc. If our AI is sufficiently careful, it might keep track of what some possible observers believe is happening. Perhaps it would simulate what other superintelligent AIs with access to its sensors would think is happening. Depending on its purview, these might be reasonable things for it to consider. If there are some possible agents that happen to often agree with the task AI (such as hypothetical superintelligent AIs with access to its sensors), our reporter might have a hard time figuring out which representations it should use to answer questions.
The task AI utilizes a parliamentarian architecture in which it's opinions are distributed over multiple specialized sub-agents, such that what it thinks is a function of those sub-agents. This has been a popular model for the human brain, so it seems conceivable that an AI would work this way. The sub-agents would have their own beliefs. Sometimes those beliefs would be wrong, but some sub-agents might be usually correct, at least in training. Consider how you would go about deciding what the US Senate currently collectively believes. A good strategy may just be to ask Joe Manchin. This strategy may stop working in 2023. Our reporter might similarly be inclined to identify one parliamentarian and report its representations. Even if the reporter decides to aggregate the representations of the relevant agents, it may have trouble figuring out how to do this correctly. There may be many good but not quite right approaches to aggregation. It isn't obvious that an AI well trained on a simple data set will succeed in finding the right way to aggregate.
2.) The Creative Interpretation Challenge
Creative interpretation is the process through which we infer implicit representations by means of explicit representations. We use creative interpretation when reading fiction or watching movies. Some aspects of the plot are not explicitly depicted, but are implied by what is depicted. For instance, movies rarely explicitly say how much time passes between scenes. We infer from contextual cues that it was a few hours, or a day, or a few weeks.
If a reporter does utilize some explicit representations, it likely won't record everything that it knows about the world explicitly. Some of its beliefs must be inferred from what it does explicitly represent and how it works with those representations. Some of its beliefs may depend on what is missing from its explicit representations or may depend on patterns of what it explicitly represents. A reporter is thus likely to need to do some creative interpretation in order to suss out the task AI's full beliefs.
Creative interpretation of a sort may be stretched even to cases where there are no explicit representations. Some beliefs may be more concrete or easier to access than others. In such cases, creative interpretation might involve the inference of less easy to access facts from more easy to access facts.
Given that a reporter needs to do some creative interpretation, the worry is that it may do more than is appropriate. Compare the legitimate creative interpretation of a novel by a careful reader with the kind of thing a lazy student who didn't read a few chapters does when writing a book report. They infer what must have happened from what they know. A reporter which is good at interpreting some things may be bad at interpreting others. If it is lazy, it may just try to figure out from context what the task AI believes given a few snippets that are particularly easy to decipher.
The challenge is to make sure that our reporter isn't inferring some of the things it translates infelicitously. It is ok if our reporter is like the average viewer of a movie. It is not ok if our reporter is like a lazy English student bullshitting through a book report. We know what distinguishes appropriate creative interpretation in books and movies. It isn't obvious how to distinguish good cases of creative inference from bad cases of inference in representational systems that we don't understand.