AGI Safety From First Principles

AGI Safety From First Principles
Richard Ngo
September 2020
This report explores the core case for why the development of articial gen-
eral intelligence (AGI) might pose an existential threat to humanity. It stems
from my dissatisfaction with existing arguments on this topic: early work is less
relevant in the context of modern machine learning, while more recent work is
scattered and brief. This report aims to ll that gap by providing a detailed
investigation into the potential risk from AGI misbehaviour, grounded by our
current knowledge of machine learning, and highlighting important uncertain-
ties. It identies four key premises, evaluates existing arguments about them,
and outlines some novel considerations for each.
Contents
1 Introduction 2
2 Superintelligence 2
2.1 Narrow and general intelligence . . . . . . . . . . . . . . . . . . . 3
2.2 Paths to superintelligence . . . . . . . . . . . . . . . . . . . . . . 5
3 Goals and Agency 8
3.1 Frameworks for thinking about agency . . . . . . . . . . . . . . . 9
3.2 The likelihood of developing highly agentic AGI . . . . . . . . . . 12
3.3 Goals as generalised concepts . . . . . . . . . . . . . . . . . . . . 14
3.4 Groups and agency . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4 Alignment 16
4.1 Outer and inner misalignment: the standard picture . . . . . . . 18
4.2 A more holistic view of alignment . . . . . . . . . . . . . . . . . . 21
5 Control 23
5.1 Disaster scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
5.2 Speed of AI development . . . . . . . . . . . . . . . . . . . . . . . 25
5.3 Transparency of AI systems . . . . . . . . . . . . . . . . . . . . . 27
5.4 Constrained deployment strategies . . . . . . . . . . . . . . . . . 28
5.5 Human political and economic coordination . . . . . . . . . . . . 28
6 Conclusion 29
7 Acknowledgements 30
1
1 Introduction
The key concern motivating technical AGI safety research is that we might build
autonomous articially intelligent agents which are much more intelligent than
humans, and which pursue goals that con
ict with our own. Human intelligence
allows us to coordinate complex societies and deploy advanced technology, and
thereby control the world to a greater extent than any other species. But AIs
will eventually become more capable than us at the types of tasks by which we
maintain and exert that control. If they don't want to obey us, then humanity
might become only Earth's second most powerful \species", and lose the ability
to create a valuable and worthwhile future.
I'll call this the \second species" argument; I think it's a plausible argument
which we should take very seriously.1However, the version stated above relies on
several vague concepts and intuitions. In this report I'll give the most detailed
presentation of the second species argument that I can, highlighting the aspects
that I'm still confused about. In particular, I'll defend a version of the second
species argument which claims that, without a concerted eort to prevent it,
there's a signicant chance that:
1. We'll build AIs which are much more intelligent than humans (i.e. super-
intelligent).
2. Those AIs will be autonomous agents which pursue large-scale goals.
3. Those goals will be misaligned with ours; that is, they will aim towards
outcomes that aren't desirable by our standards, and trade o against our
goals.
4. The development of such AIs would lead to them gaining control of hu-
manity's future.
While I use many examples from modern deep learning, this report is also
intended to apply to AIs developed using very dierent models, training algo-
rithms, optimisers, or training regimes than the ones we use today. However,
many of my arguments would no longer be relevant if the eld of AI moves away
from focusing on machine learning. I also frequently compare AI development
to the evolution of human intelligence; while the two aren't fully analogous,
humans are the best example we currently have to ground our thinking about
generally intelligent AIs.
2 Superintelligence
In order to understand superintelligence, we should rst characterise what we
mean by intelligence. We can start with Legg and Hutter [2007]'s well-known
denition, which identies intelligence as the ability to achieve goals in a wide
1Stuart Russell also refers to this as the \gorilla problem" in his recent book, Human
Compatible [Russell, 2019].
2
range of environments.2However, there are multiple ways to score highly on
this metric. The key distinction I'll draw in this section is between agents that
understand how to do well at many tasks because they have been specically
optimised for each task (which I'll call the task-based approach to AI), versus
agents which can understand new tasks with little or no task-specic training,
by generalising from previous experience (the generalisation-based approach).
2.1 Narrow and general intelligence
The task-based approach is analogous to how humans harnessed electricity:
while electricity is a powerful and general technology, we still need to design
specic ways to apply it to each task. Similarly, computers are powerful and

exible tools - but even though they can process arbitrarily many dierent in-
puts, detailed instructions for how to do that processing needs to be individually
written to build each piece of software. Meanwhile our current reinforcement
learning algorithms, although powerful, produce agents that are only able to
perform well on specic tasks at which they have a lot of experience - Starcraft,
DOTA, Go, and so on. Drexler [2019] argues that our current task-based ap-
proach will scale up to allow superhuman performance on a range of complex
tasks (although I'm skeptical of this claim).3
An example of the generalisation-based approach can be found in large lan-
guage models like GPT-2 and GPT-3. GPT-2 was rst trained on the task of
predicting the next word in a corpus, and then achieved state of the art results
on many other language tasks, without any task-specic ne-tuning! [Radford
et al., 2019] This was a clear change from previous approaches to natural lan-
guage processing, which only scored well when trained to do specic tasks on
specic datasets. Its successor, GPT-3, has displayed a range of even more im-
pressive behaviour [Sotala, 2020]. I think this provides a good example of how
an AI could develop cognitive skills (in this case, an understanding of the syntax
and semantics of language) which generalise to a range of novel tasks. The eld
of meta-learning aims towards a similar goal.
We can also see the potential of the generalisation-based approach by looking
at how humans developed. As a species, we were \trained" by evolution to
have cognitive skills including rapid learning capabilities; sensory and motor
processing; and social skills. As individuals, we were also \trained" during our
childhoods to ne-tune those skills; to understand spoken and written language;
and to possess detailed knowledge about modern society. However, the key
point is that almost all of this evolutionary and childhood learning occurred
on dierent tasks from the economically useful ones we perform as adults. We
can perform well on the latter category only by reusing the cognitive skills and
knowledge that we gained previously. In our case, we were fortunate that those
2Unlike the standard usage, in this technical sense an \environment" also includes a speci-
cation of the input-output channels the agent has access to (such as motor outputs), so that
solving the task only requires an agent to process input information and communicate output
information.
3For reasons outlined in Ngo [2019a].
3
cognitive skills were not too specic to tasks in the ancestral environment, but
were rather very general skills. In particular, the skill of abstraction allows
us to extract common structure from dierent situations, which allows us to
understand them much more eciently than by learning about them one by
one. Then our communication skills and theories of mind allow us to share our
ideas. This is why humans can make great progress on the scale of years or
decades, not just via evolutionary adaptation over many lifetimes.
I should note that I think of task-based and generalisation-based as parts of
a spectrum rather than a binary classication, particularly because the way we
choose how to divide up tasks can be quite arbitrary. For example, AlphaZero
trained by playing against itself, but was tested by playing against humans, who
use dierent strategies and playing styles. We could think of playing against
these two types of opponents as two instances of a single task, or as two sep-
arate tasks where AlphaZero was able to generalise from the former task to
the latter. But either way, the two cases are clearly very similar. By contrast,
there are many economically important tasks which I expect AI systems to do
well at primarily by generalising from their experience with very dierent tasks
- meaning that those AIs will need to generalise much, much better than our
current reinforcement learning systems can.
Let me be more precise about the tasks which I expect will require this new
regime of generalisation. To the extent that we can separate the two approaches,
it seems plausible to me that the task-based approach will get a long way in
areas where we can gather a lot of data. For example, I'm condent that it will
produce superhuman self-driving cars well before the generalisation-based ap-
proach does so. It may also allow us to automate most of the tasks involved even
in very cognitively demanding professions like medicine, law, and mathematics,
if we can gather the right training data. However, some jobs crucially depend
on the ability to analyse and act on such a wide range of information that it'll
be very dicult to train directly for high performance on them. Consider the
tasks involved in a role like CEO: setting your company's strategic direction,
choosing who to hire, writing speeches, and so on. Each of these tasks sensi-
tively depends on the broader context of the company and the rest of the world.
What industry is their company in? How big is it; where is it; what's its cul-
ture like? What's its relationship with competitors and governments? How will
all of these factors change over the next few decades? These variables are so
broad in scope, and rely on so many aspects of the world, that it seems virtu-
ally impossible to generate large amounts of training data via simulating them
(like we do to train game-playing AIs). And the number of CEOs from whom
we could gather empirical data is very small by the standards of reinforcement
learning (which often requires billions of training steps even for much simpler
tasks). I'm not saying that we'll never be able to exceed human performance
on these tasks by training on them directly - maybe a herculean research and
engineering eort, assisted by other task-based AIs, could do so. But I expect
that well before such an eort becomes possible, we'll have built AIs using the
generalisation-based approach which know how to perform well even on these
broad tasks.
4
In the generalisation-based approach, the way to create superhuman CEOs
is to use other data-rich tasks (which may be very dierent from the tasks
we actually want an AI CEO to do) to train AIs to develop a range of useful
cognitive skills. For example, we could train a reinforcement learning agent
to follow instructions in a simulated world. Even if that simulation is very
dierent from the real world, that agent may acquire the planning and learning
capabilities required to quickly adapt to real-world tasks. Analogously, the
human ancestral environment was also very dierent to the modern world, but
we are still able to become good CEOs with little further training. And roughly
the same argument applies to people doing other highly impactful jobs, like
paradigm-shaping scientists, entrepreneurs, or policymakers.
One potential obstacle to the generalisation-based approach succeeding is
the possibility that specic features of the ancestral environment, or of human
brains, were necessary for general intelligence to arise [Ngo, 2020b]. For ex-
ample, Dunbar [1998] hypothesised that a social \arms race" was required to
give us enough social intelligence to develop large-scale cultural transmission.
However, most possibilities for such crucial features, including this one, could
be recreated in articial training environments and in articial neural networks.
Some features (such as quantum properties of neurons) would be very hard to
simulate precisely, but the human brain operates under conditions that are too
messy to make it plausible that our intelligence depends on eects at this scale.
So it seems very likely to me that eventually we will be able to create AIs that
can generalise well enough to produce human-level performance on a wide range
of tasks, including abstract low-data tasks like running a company. Let's call
these systems articial general intelligences, or AGIs. Many AI researchers ex-
pect that we'll build AGI within this century [Grace et al., 2018]; however, I
won't explore arguments around the timing of AGI development, and the rest
of this document doesn't depend on this question.
2.2 Paths to superintelligence
Bostrom [2014] denes a superintelligence as \any intellect that greatly exceeds
the cognitive performance of humans in virtually all domains of interest". For
the purposes of this report, I'll operationalise \greatly exceeding human perfor-
mance" as doing better than all of humanity could if we coordinated globally
(unaided by other advanced AI). I think it's dicult to deny that in principle
it's possible to build individual generalisation-based AGIs which are superintel-
ligent, since human brains are constrained by many factors4which will be much
less limiting for AIs. Perhaps the most striking is the vast dierence between
the speeds of neurons and transistors: the latter pass signals about four million
times more quickly. Even if AGIs never exceed humans in any other way, a
speedup this large would allow one to do as much thinking in minutes or hours
as a human can in years or decades. Meanwhile our brain size is important in
making humans more capable than most animals - but I don't see any reason
4Muehlhauser [2013]
5
why a neural network couldn't be several orders of magnitude larger than a
human brain. And while evolution is a very capable designer in many ways, it
hasn't had much time to select specically for the skills that are most useful
in our modern environment, such as linguistic competence and mathematical
reasoning. So we should expect that there are low-hanging fruit for improving
on human performance on the many tasks which rely on such skills.5
There are signicant disagreements about how long it will take to transition
from human-level AGI to superintelligence, which won't be a focus of this re-
port, but which I'll explore brie
y in the section on Control. In the remainder
of this section I'll describe in qualitative terms how this transition might occur.
By default, we should expect that it will be driven by the standard factors which
in
uence progress in AI: more compute, better algorithms, and better training
data. But I'll also discuss three factors whose contributions to increasing AI in-
telligence will become much greater as AIs become more intelligent: replication,
cultural learning, and recursive improvement.
In terms of replication, AIs are much less constrained than humans: it's very
easy to create a duplicate of an AI which has all the same skills and knowledge
as the original. The cost of compute for doing so is likely to be many times
smaller than the original cost of training an AGI (since training usually involves
running many copies of an AI much faster than they'd need to be run for real-
world tasks). Duplication currently allows us to apply a single AI to many tasks,
but not to expand the range of tasks which that AI can achieve. However, we
should expect AGIs to be able to decompose dicult tasks into subtasks which
can be tackled more easily, just as humans can. So duplicating such an AGI
could give rise to a superintelligence composed not of a single AGI, but rather
a large group of them (which, following Bostrom [2014], I'll call a collective
AGI), which can carry out signicantly more complex tasks than the original
can.6Because of the ease and usefulness of duplicating an AGI, I think that
collective AGIs should be our default expectation for how superintelligence will
be deployed.
The ecacy of a collective AGI might be limited by coordination problems
between its members. However, most of the arguments given in the previous
paragraphs are also reasons why individual AGIs will be able to surpass us at
the skills required for coordination (such as language processing and theories
of mind). One particularly useful skill is cultural learning: we should expect
AGIs to be able to acquire knowledge from each other and then share their own
discoveries in turn, allowing a collective AGI to solve harder problems than any
individual AGI within it could. The development of this ability in humans is
5This observation is closely related to Moravec's paradox, which I discuss in more detail
in the section on Goals and Agency. Perhaps the most salient example is how easy it was for
AIs to beat humans at chess.
6It's not quite clear whether the distinction between \single AGIs" and collective AGIs
makes sense in all cases, considering that a single AGI can be composed of many modules
which might be very intelligent in their own right. But since it seems unlikely that there will
be hundreds or thousands of modules which are each generally intelligent, I think that the
distinction will in practice be useful. See also Ngo [2020a] and the discussion of \collective
superintelligence" in Bostrom [2014].
6
what allowed the dramatic rise of civilisation over the last ten thousand years.
Yet there is little reason to believe that we have reached the peak of this ability,
or that AGIs couldn't have a much larger advantage over a human than that
human has over a chimp, in acquiring knowledge from other agents.
Thirdly, AGIs will be able to improve the training processes used to develop
their successors, which then improve the training processes used to develop their
successors, and so on, in a process of recursive improvement .7Previous discus-
sion has mostly focused on recursive self-improvement, involving a single AGI
\rewriting its own source code" [Yudkowsky, 2007]. However, I think it's more
appropriate to focus on the broader phenomenon of AIs advancing AI research,
for several reasons. Firstly, due to the ease of duplicating AIs, there's no mean-
ingful distinction between an AI improving \itself" versus creating a successor
that shares many of its properties. Secondly, modern AIs are more accurately
characterised as models which could be retrained, rather than software which
could be rewritten: almost all of the work of making a neural network intelli-
gent is done by an optimiser via extensive training. Even a superintelligent AGI
would have a hard time signicantly improving its cognition by modifying its
neural weights directly; it seems analogous to making a human more intelligent
via brain surgery (albeit with much more precise tools than we have today). So
it's probably more accurate to think about self-modication as the process of
an AGI modifying its high-level architecture or training regime, then putting
itself through signicantly more training. This is very similar to how we create
new AIs today, except with humans playing a much smaller role. Thirdly, if the
intellectual contribution of humans does shrink signicantly, then I don't think
it's useful to require that humans are entirely out of the loop for AI behaviour
to qualify as recursive improvement (although we can still distinguish between
cases with more or less human involvement).
These considerations reframe the classic view of recursive self-improvement8
in a number of ways. For example, the retraining step may be bottlenecked by
compute even if an AGI is able to design algorithmic improvements very fast.
And for an AGI to trust that its goals will remain the same under retraining will
likely require it to solve many of the same problems that the eld of AGI safety
is currently tackling - which should make us more optimistic that the rest of the
world could solve those problems before a misaligned AGI undergoes recursive
self-improvement. However, to be clear, this reframing doesn't imply that re-
cursive improvement will be unimportant. Indeed, since AIs will eventually be
the primary contributors to AI research, recursive improvement as dened here
will eventually become the key driver of progress. I'll discuss the implications
of this claim in the section on Control.
So far I've focused on how superintelligences might come about, and what
they will be able to do. But how will they decide what to actually do? For
example, will the individuals within a collective AGI even want to cooperate
7Whether it's more likely that the successor agent will be an augmented version of the
researcher AGI itself or a dierent, newly-trained AGI is an important question, but one
which doesn't aect the argument as made here.
8Yudkowsky [2013]
7
with each other to pursue larger goals? Will an AGI capable of recursive im-
provement have any reason to do so? I'm wary of phrasing these questions in
terms of the goals and motivations of AGIs, without exploring more thoroughly
what those terms actually mean. That's the focus of the next section.
3 Goals and Agency
The fundamental concern motivating the second species argument is that AIs
will gain too much power over humans, and then use that power in ways we
don't endorse. Why might they end up with that power? I'll distinguish three
possibilities:
1. AIs pursue power for the sake of achieving other goals; i.e. power is an
instrumental goal for them.
2. AIs pursue power for its own sake; i.e. power is a nal goal for them.
3. AIs gain power without aiming towards it; e.g. because humans gave it to
them.
The rst possibility has been the focus of most debate so far, and I'll spend
most of this section discussing it. The second hasn't been explored in much
depth, but in my opinion is still important; I'll cover it brie
y in this section
and the next. Following Christiano [2019], I'll call agents which fall into either
of these rst two categories in
uence-seeking . The third possibility is largely
outside the scope of this document, which focuses on dangers from the inten-
tional behaviour of advanced AIs, although I'll brie
y touch on it here and in
the last section.
The key idea behind the rst possibility is Bostrom [2012]'s instrumental
convergence thesis, which states that there are some instrumental goals whose
attainment would increase the chances of an agent's nal goals being realised
for a wide range of nal goals and a wide range of situations. Examples of
such instrumentally convergent goals include self-preservation, resource acquisi-
tion, technological development, and self-improvement, which are all useful for
executing further large-scale plans. I think these examples provide a good char-
acterisation of the type of power I'm talking about, which will serve in place of
a more explicit denition.
However, the link from instrumentally convergent goals to dangerous in
uence-
seeking is only applicable to agents which have nal goals large-scale enough to
benet from these instrumental goals, and which identify and pursue those in-
strumental goals even when it leads to extreme outcomes (a set of traits which
I'll call goal-directed agency ). It's not yet clear that AGIs will be this type of
agent, or have this type of goals. It seems very intuitive that they will because
we all have experience of pursuing instrumentally convergent goals, for exam-
ple by earning and saving money, and can imagine how much better we'd be
at them if we were more intelligent. Yet since evolution has ingrained in us
8
many useful short-term drives (in particular the drive towards power itself), it's
dicult to determine the extent to which human in
uence-seeking behaviour is
caused by us reasoning about its instrumental usefulness towards larger-scale
goals. Our conquest of the world didn't require any humans to strategise over
the timeframe of centuries, but merely for many individuals to expand their
personal in
uence in a relatively limited way - by inventing a slightly better
tool, or exploring slightly further aeld.
Furthermore, we should take seriously the possibility that superintelligent
AGIs might be even less focused than humans are on achieving large-scale goals.
We can imagine them possessing nal goals which don't incentivise the pursuit
of power, such as deontological goals, or small-scale goals. Or perhaps we'll
build \tool AIs" which obey our instructions very well without possessing goals
of their own - in a similar way to how a calculator doesn't \want" to answer
arithmetic questions, but just does the calculations it's given. In order to gure
out which of these options is possible or likely, we need to better understand
the nature of goals and goal-directed agency. That's the focus of this section.
3.1 Frameworks for thinking about agency
To begin, it's crucial to distinguish between the goals which an agent has been
selected ordesigned to do well at (which I'll call its design objectives9), and the
goals which an agent itself wants to achieve (which I'll just call \the agent's
goals").10For example, insects can contribute to complex hierarchical soci-
eties only because evolution gave them the instincts required to do so: to have
\competence without comprehension", in Dennett's terminology. This term
also describes current image classiers and (probably) RL agents like AlphaStar
and OpenAI Five: they can be competent at achieving their design objectives
without understanding what those objectives are, or how their actions will help
achieve them. If we create agents whose design objective is to accumulate power,
but without the agent itself having the goal of doing so (e.g. an agent which
plays the stock market very well without understanding how that impacts soci-
ety) that would qualify as the third possibility outlined above.
By contrast, in this section I'm interested in what it means for an agent
to have a goal of its own. Three existing frameworks which attempt to an-
swer this question are Morgenstern and Von Neumann [1953]'s expected utility
maximisation , Dennett [1989]'s intentional stance , and Hubinger et al. [2019]'s
mesa-optimisation . I don't think any of them adequately characterises the type
of goal-directed behaviour we want to understand, though. While we can prove
elegant theoretical results about utility functions, they are such a broad formal-
ism that practically any behaviour can be described as maximising some utility
function [Ngo, 2019b]. So this framework doesn't constrain our expectations
about powerful AGIs.11Meanwhile, Dennett argues that taking the intentional
9Following Ortega et al. [2018].
10AI systems which learn to pursue goals are also known as mesa-optimisers , as coined in
Hubinger et al. [2019].
11Related arguments exist which attempt to do so. For example, Yudkowsky [2018] argues
9
stance towards systems can be useful for making predictions about them - but
this only works given prior knowledge about what goals they're most likely to
have. Predicting the behaviour of a trillion-parameter neural network is very
dierent from applying the intentional stance to existing artifacts. And while
we do have an intuitive understanding of complex human goals and how they
translate to behaviour, the extent to which it's reasonable to extend those be-
liefs about goal-directed cognition to articial intelligences is the very question
we need a theory of agency to answer. So while Dennett's framework provides
some valuable insights - in particular, that assigning agency to a system is a
modelling choice which only applies at certain levels of abstraction - I think it
fails to reduce agency to simpler and more tractable concepts.
Additionally, neither framework accounts for bounded rationality: the idea
that systems can be \trying to" achieve a goal without taking the best actions
to do so. In order to gure out the goals of boundedly rational systems, we'll
need to scrutinise the structure of their cognition, rather than treating them
as black-box functions from inputs to outputs - in other words, using a \cogni-
tive" denition of agency rather than \behavioural" denitions like the two I've
discussed so far. In Risks from Learned Optimisation in Advanced ML systems ,
Hubinger et al. [2019] use a cognitive denition: \a system is an optimizer if
it is internally searching through a search space (consisting of possible outputs,
policies, plans, strategies, or similar) looking for those elements that score high
according to some objective function that is explicitly represented within the
system". I think this is a promising start, but it has some signicant problems.
In particular, the concept of \explicit representation" seems like a tricky one -
what is explicitly represented within a human brain, if anything? And their def-
inition doesn't draw the important distinction between \local" optimisers such
as gradient descent and goal-directed planners such as humans.
My own approach to thinking about agency tries to improve on the ap-
proaches above by being more specic about the cognition we expect goal-
directed systems to do. Just as \being intelligent" involves applying a range
of abilities (as discussed in the previous section), \being goal-directed" involves
a system applying some specic additional abilities:
1.Self-awareness : it understands that it's a part of the world, and that its
behaviour impacts the world;
2.Planning : it considers a wide range of possible sequences of behaviours
(let's call them \plans"), including long plans;
3.Consequentialism : it decides which of those plans is best by considering
the value of the outcomes that they produce;
that, \while corrigibility probably has a core which is of lower algorithmic complexity than all
of human value, this core is liable to be very hard to nd or reproduce by supervised learning
of human-labeled data, because deference is an unusually anti-natural shape for cognition,
in a way that a simple utility function would not be an anti-natural shape for cognition."
Note, however, that this argument relies on the intuitive distinction between natural and
anti-natural shapes for cognition. This is precisely what I think we need to understand to
build safe AGI - but there has been little explicit investigation of it so far.
10
4.Scale : its choice is sensitive to the eects of plans over large distances and
long time horizons;
5.Coherence : it is internally unied towards implementing the single plan
it judges to be best;
6.Flexibility : it is able to adapt its plans
exibly as circumstances change,
rather than just continuing the same patterns of behaviour.
Note that none of these traits should be interpreted as binary; rather, each
one denes a dierent spectrum of possibilities. I'm also not claiming that the
combination of these six dimensions is a precise or complete characterisation
of agency; merely that it's a good starting point, and the right type of way to
analyse agency. For instance, it highlights that agency requires a combination
of dierent abilities - and as a corollary, that there are many dierent ways to
be less than maximally agentic. AIs which score very highly on some of these
dimensions might score very low on others. Considering each trait in turn, and
what lacking it might look like:
1.Self-awareness : for humans, intelligence seems intrinsically linked to a
rst-person perspective. But an AGI trained on abstract third-person
data might develop a highly sophisticated world-model that just doesn't
include itself or its outputs. A suciently advanced language or physics
model might t into this category.
2.Planning : highly intelligent agents will by default be able to make exten-
sive and sophisticated plans. But in practice, like humans, they may not
always apply this ability. Perhaps, for instance, an agent is only trained to
consider restricted types of plans. Myopic training attempts to implement
such agents; more generally, an agent could have limits on the actions it
considers. For example, a question-answering system might only consider
plans of the form \rst gure out subproblem 1, then gure out subprob-
lem 2, then...".
3.Consequentialism : the usual use of this term in philosophy describes
agents which believe that the moral value of their actions depends only on
those actions' consequences; here I'm using it in a more general way, to de-
scribe agents whose subjective preferences about actions depend mainly
on those actions' consequences. It seems natural to expect that agents
trained on a reward function determined by the state of the world would
be consequentialists. But note that humans are far from fully consequen-
tialist, since we often obey deontological constraints or constraints on the
types of reasoning we endorse.
4.Scale : agents which only care about small-scale events may ignore the
long-term eects of their actions. Since agents are always trained in small-
scale environments, developing large-scale goals requires generalisation (in
ways that I discuss below).
11
5.Coherence : humans lack this trait when we're internally con
icted - for
example, when our system 1 and system 2 goals dier - or when our goals
change a lot over time. While our internal con
icts might just be an
artefact of our evolutionary history, we can't rule out individual AGIs de-
veloping modularity which might lead to comparable problems. However,
it's most natural to think of this trait in the context of a collective, where
the individual members could have more or less similar goals, and could
be coordinated to a greater or lesser extent.
6.Flexibility : an in
exible agent might arise in an environment in which
coming up with one initial plan is usually sucient, or else where there
are tradeos between making plans and executing them. Such an agent
might display sphexish behaviour. Another interesting example might be
a multi-agent system in which many AIs contribute to developing plans -
such that a single agent is able to execute a given plan, but not able to
rethink it very well.
A question-answering system (aka an oracle) could be implemented by an
agent lacking either planning or consequentialism. For AIs which act in the real
world I think the scale of their goals is a crucial trait to explore, and I'll do so
later in this section. We can also evaluate other systems on these criteria. A
calculator probably doesn't have any of them. Software that's a little more com-
plicated, like a GPS navigator, should probably be considered consequentialist
to a limited extent (because it reroutes people based on how congested trac
is), and perhaps has some of the other traits too, but only slightly. Most animals
are self-aware, consequentialist and coherent to various degrees. The traditional
conception of AGI has all of the traits above, which would make it capable of
pursuing in
uence-seeking strategies for instrumental reasons. However, note
that goal-directedness is not the only factor which determines whether an AI is
in
uence-seeking: the content of its goals also matter. A highly agentic AI which
has the goal of remaining subordinate to humans might never take in
uence-
seeking actions. And as previously mentioned, an AI might be in
uence-seeking
because it has the nal goal of gaining power, even without possessing many of
the traits above. I'll discuss ways to in
uence the content of an agent's goals in
the next section, on Alignment.
3.2 The likelihood of developing highly agentic AGI
How likely is it that, in developing an AGI, we produce a system with all of the
six traits I identied above? One approach to answering this question involves
predicting which types of model architecture and learning algorithms will be
used - for example, will they be model-free or model-based? To my mind, this
line of thinking is not abstract enough, because we simply don't know enough
about how cognition and learning work to map them onto high-level design
choices. If we train AGI in a model-free way, I predict it will end up planning
12
using an implicit model12anyway. If we train a model-based AGI, I predict its
model will be so abstract and hierarchical that looking at its architecture will
tell us very little about the actual cognition going on.
At a higher level of abstraction, I think that it'll be easier for AIs to acquire
the components of agency listed above if they're also very intelligent. However,
the extent to which our most advanced AIs are agentic will depend on what
type of training regime is used to produce them. For example, our best language
models already generalise well enough from their training data that they can
answer a wide range of questions. I can imagine them becoming more and
more competent via unsupervised and supervised training, until they are able
to answer questions which no human knows the answer to, but still without
possessing any of the properties listed above. A relevant analogy might be to
the human visual system, which does very useful cognition, but which is not
very \goal-directed" in its own right.
My underlying argument is that agency is not just an emergent property
of highly intelligent systems, but rather a set of capabilities which need to be
developed during training, and which won't arise without selection for it. One
piece of supporting evidence is Moravec's paradox: the observation that the
cognitive skills which seem most complex to humans are often the easiest for
AIs, and vice versa [Moravec, 1988]. In particular, Moravec's paradox predicts
that building AIs which do complex intellectual work like scientic research
might actually be easier than building AIs which share more deeply ingrained
features of human cognition like goals and desires. To us, understanding the
world and changing the world seem very closely linked, because our ancestors
were selected for their ability to act in the world to improve their situations.
But if this intuition is
awed, then even reinforcement learners may not develop
all the aspects of goal-directedness described above if they're primarily trained
to answer questions.
However, there are also arguments that it will be dicult to train AIs to
do intellectual work without them also developing goal-directed agency. In the
case of humans, it was the need to interact with an open-ended environment13
to achieve our goals that pushed us to develop our sophisticated general intelli-
gence. The central example of an analogous approach to AGI is training a re-
inforcement learning agent in a complex simulated 3D environment (or perhaps
via extended conversations in a language-only setting). In such environments,
agents which strategise about the eects of their actions over long time horizons
will generally be able to do better. This implies that our AIs will be subject
to optimisation pressure towards becoming more agentic (by my criteria above)
will do better. We might expect an AGI to be even more agentic if it's trained,
not just in a complex environment, but in a complex competitive multi-agent
environment. Agents trained in this way will need to be very good at
exibly
adapting plans in the face of adversarial behaviour; and they'll benet from
considering a wider range of plans over a longer timescale than any competitor.
12As in Guez et al. [2019].
13A concept explored further in Ecoet et al. [2020].
13
On the other hand, it seems very dicult to predict the overall eect of interac-
tions between many agents - in humans, for example, it led to the development
of (sometimes non-consequentialist) altruism.
It's currently very uncertain which training regimes will work best to produce
AGIs. But if there are several viable ones, we should expect economic pressures
to push researchers towards prioritising those which produce the most agentic
AIs, because they will be the most useful (assuming that alignment problems
don't become serious until we're close to AGI). In general, the broader the task
an AI is used for, the more valuable it is for that AI to reason about how
to achieve its assigned goal in ways that we may not have specically trained
it to do. For example, a question-answering system with the goal of helping
its users understand the world might be much more useful than one that's
competent at its design objective of answering questions accurately, but isn't
goal-directed in its own right. Overall, however, I think most safety researchers
would argue that we should prioritise research directions which produce less
agentic AGIs, and then use the resulting AGIs to help us align later more agentic
AGIs. There's also been some work on directly making AGIs less agentic (such
as Taylor [2016]'s quantilization), although this has in general been held back
by a lack of clarity around these concepts.
I've already discussed recursive improvement in the previous section, but
one further point which is useful to highlight here: since being more agentic
makes an agent more capable of achieving its goals, agents which are capable of
modifying themselves will have incentives to make themselves more agentic too
(as humans already try to do, albeit in limited ways). So we should consider this
to be one type of recursive improvement, to which many of the considerations
discussed in the previous section also apply.
3.3 Goals as generalised concepts
I should note that I don't expect our training tasks to replicate the scale or
duration of all the tasks we care about in the real world. So AGIs won't be
directly selected to have very large-scale or long-term goals. Yet it's likely that
the goals they learn in their training environments will generalise to larger scales,
just as humans developed large-scale goals from evolving in a relatively limited
ancestral environment. In modern society, people often spend their whole lives
trying to signicantly in
uence the entire world - via science, business, politics,
and many other channels. And some people aspire to have a worldwide impact
over the timeframe of centuries, millennia or longer, even though there was
never signicant evolutionary selection in favour of humans who cared about
what happened in several centuries' time, or paid attention to events on the
other side of the world. This gives us reason to be concerned that even AGIs
which aren't explicitly trained to pursue ambitious large-scale goals might do
so anyway. I also expect researchers to actively aim towards achieving this
type of generalisation to longer time horizons in AIs, because some important
applications rely on it. For long-term tasks like being a CEO, AGIs will need
the capability and motivation to choose between possible actions based on their
14
worldwide consequences on the timeframe of years or decades.
Can we be more specic about what it looks like for goals to generalise to
much larger scales? Given the problems with the expected utility maximisation
framework I identied earlier, it doesn't seem useful to think of goals as utility
functions over states of the world. Rather, an agent's goals can be formulated
in terms of whatever concepts it possesses - regardless of whether those con-
cepts refer to its own thought processes, deontological rules, or outcomes in the
external world.14And insofar as an agent's concepts
exibly adjust and gen-
eralise to new circumstances, the goals which refer to them will do the same.
It's dicult and speculative to try to describe how such generalisation may oc-
cur, but broadly speaking, we should expect that intelligent agents are able to
abstract away the dierences between objects or situations that have high-level
similarities. For example, after being trained in a simulation, an agent might
transfer its attitudes towards objects and situations in the simulation to their
counterparts in the (much larger) real world.15Alternatively, the generalisation
could be in the framing of the goal: an agent which has always been rewarded
for accumulating resources in its training environment might interalise the goal
of \amassing as many resources as possible". Similarly, agents which are trained
adversarially in a small-scale domain might develop a goal of outcompeting each
other which persists even when they're both operating at a very large scale.
From this perspective, to predict an agent's behaviour, we will need to con-
sider what concepts it will possess, how those will generalise, and how the agent
will reason about them. I'm aware that this appears to be an intractably di-
cult task - even human-level reasoning can lead to extreme and unpredictable
conclusions (as the history of philosophy shows). However, I hope that we can
instill lower-level mindsets or values into AGIs which guide their high-level rea-
soning in safe directions. I'll discuss some approaches to doing so in the next
section, on Alignment.
3.4 Groups and agency
After discussing collective AGIs in the previous section, it seems important to
examine whether the framework I've proposed for understanding agency can
apply to a group of agents as well. I think it can: there's no reason that the
traits I described above need to be instantiated within a single neural network.
However, the relationship between the goal-directedness of a collective AGI and
the goal-directedness of its individual members may not be straightforward,
since it depends on the internal interactions between its members.
14For example, when people want to be \cooperative" or \moral", they're often not just
thinking about results, but rather the types of actions they should take, or the types of
decision procedures they should use to generate those actions. An additional complication
is that humans don't have full introspective access to all our concepts - so we need to also
consider unconscious concepts.
15Consider if this happened to you, and you were pulled \out of the simulation" into a
real world which is quite similar to what you'd already experienced. By default you would
likely still want to eat good food, have fullling relationships, and so on, despite the radical
ontological shift you just underwent.
15
One of the key variables is how much (and what types of) experience those
members have of interacting with each other during training. If they have been
trained primarily to cooperate, that makes it more likely that the resulting col-
lective AGI is a goal-directed agent, even if none of the individual members
is highly agentic. But there are good reasons to expect that the training pro-
cess will involve some competition between members, which would undermine
their coherence as a group [Leibo et al., 2019]. Internal competition might also
increase short-term in
uence-seeking behaviour, since each member will have
learned to pursue in
uence in order to outcompete the others. As a particularly
salient example, humanity managed to take over the world over a period of mil-
lennia not via a unied plan to do so, but rather as a result of many individuals
trying to expand their short-term in
uence.
It's also possible that the members of a collective AGI have not been trained
to interact with each other at all, in which case cooperation between them
would depend entirely on their ability to generalise from their existing skills. It's
dicult to imagine this case, because human brains are so well-adapted for group
interactions. But insofar as humans and aligned AGIs hold a disproportionate
share of power over the world, there is a natural incentive for AGIs pursuing
misaligned goals to coordinate with each other to increase their in
uence at
our expense.16Whether they succeed in doing so will depend on what sort of
coordination mechanisms they are able to design.
A second factor is how much specialisation there is within the collective AGI.
In the case where it consists only of copies of the same agent, we should expect
that the copies understand each other very well, and share goals to a large
extent. If so, we might be able to make predictions about the goal-directedness
of the entire group merely by examining the original agent. But another case
worth considering is a collective consisting of a range of agents with dierent
skills. With this type of specialisation17, the collective as a whole could be much
more agentic than any individual agent within it, which might make it easier to
deploy subsets of the collective safely [Ngo, 2020e].
4 Alignment
In the previous section, I discussed the plausibility of ML-based agents devel-
oping the capability to seek in
uence for instrumental reasons. This would not
be a problem if they do so only in the ways that are aligned with human val-
ues. Indeed, many of the benets we expect from AGIs will require them to
wield power to in
uence the world. And by default, AI researchers will apply
16In addition to the prima facie argument that intelligence increases coordination ability,
it is likely that AGIs will have access to commitment devices not available to humans by
virtue of being digital. For example, they could send potential allies a copy of themselves
for inspection, to increase condence in their trustworthiness. However, there are also human
commitment devices that AGIs will have less access to - for example, putting ourselves in
physical danger as an honest signal. And it's possible that the relative diculty of lying
versus detecting lying shifts in favour of the former for more intelligent agents.
17Ngo [2020d]
16
their eorts towards making agents do whatever tasks those researchers desire,
rather than learning to be disobedient. However, there are reasons to worry
that despite such eorts by AI researchers, AIs will develop undesirable nal
goals which lead to con
ict with humans.
To start with, what does \aligned with human values" even mean? Follow-
ing Christiano [2015] and Gabriel [2020], I'll distinguish between two types of
interpretations. Minimalist (aka narrow ) approaches focus on avoiding catas-
trophic outcomes. The best example is Christiano [2018a]'s concept of intent
alignment: \When I say an AI A is aligned with an operator H, I mean: A is
trying to do what H wants it to do ." While there will always be some edge cases
in guring out a given human's intentions, there is at least a rough commonsense
interpretation. By contrast, maximalist (aka ambitious ) approaches attempt to
make AIs adopt or defer to a specic overarching set of values - like a particular
moral theory, or a global democratic consensus, or a meta-level procedure for
deciding between moral theories.
My opinion is that dening alignment in maximalist terms is unhelpful,
because it bundles together technical, ethical and political problems. While it
may be the case that we need to make progress on all of these, assumptions
about the latter two can signicantly reduce clarity about technical issues. So
from now on, when I refer to alignment, I'll only refer to intent alignment. I'll
also dene an AI A to be misaligned with a human H if H would want A not
to do what A is trying to do (if H were aware of A's intentions). This implies
that AIs could potentially be neither aligned nor misaligned with an operator
- for example, if they only do things which the operator doesn't care about.
Whether an AI qualies as aligned or misaligned obviously depends a lot on
who the operator is, but for the purposes of this report I'll focus on AIs which
are clearly misaligned with respect to most humans.
One important feature of these denitions: by using the word \trying", they
focus on the AI's intentions, not the actual outcomes achieved. I think this
makes sense because we should expect AGIs to be very good at understanding
the world, and so the key safety problem is setting their intentions correctly.
In particular, I want to be clear that when I talk about misaligned AGI, the
central example in my mind is not agents that misbehave just because they mis-
understand what we want, or interpret our instructions overly literally (which
Bostrom [2014] calls \perverse instantiation"). It seems likely that AGIs will un-
derstand the intentions of our instructions very well by default. This is because
they will probably be trained on tasks involving humans, and human data - and
understanding human minds is particularly important for acting competently in
those tasks and the rest of the world.18Rather, my main concern is that AGIs
will understand what we want, but just not care, because the motivations they
acquired during training weren't those we intended them to have.
The idea that AIs won't automatically gain the right motivations by virtue of
being more intelligent is an implication of Bostrom [2012]'s orthogonality thesis,
18Of course, what humans say we want, and what we act as if we want, and what we privately
desire often diverge. But again, I'm not particularly worried about a superintelligence being
unable to understand how humans distinguish between these categories, if it wanted to.
17
which states that \more or less any level of intelligence could in principle be
combined with more or less any nal goal". For our purposes, a weaker version
suces: simply that highly intelligent agents could have large-scale goals which
are misaligned with those of most humans. An existence proof is provided by
high-functioning psychopaths, who understand that other people are motivated
by morality, and can use that fact to predict their actions and manipulate them,
but nevertheless aren't motivated by morality themselves.
We might hope that by carefully choosing the tasks on which agents are
trained, we can prevent those agents from developing goals that con
ict with
ours, without requiring any breakthroughs in technical safety research. Why
might this not work, though? Previous arguments have distinguished between
two concerns: the outer misalignment problem and the inner misalignment
problem. I'll explain both of these, and give arguments for why they might arise.
I'll also discuss some limitations of using this framework, and an alternative
perspective on alignment.
4.1 Outer and inner misalignment: the standard picture
We train machine learning systems to perform desired behaviour by optimising
them with respect to some objective function - for example, a reward function
in reinforcement learning. The outer misalignment concern is that we won't be
able to implement an objective function which describes the behaviour we ac-
tually want the system to perform, without also rewarding misbehaviour. One
key intuition underlying this concern is the diculty of explicitly programming
objective functions which express all our desires about AGI behaviour. There's
no simple metric which we'd like our agents to maximise - rather, desirable AGI
behaviour is best formulated in terms of concepts like obedience, consent, help-
fulness, morality, and cooperation, which we can't dene precisely in realistic
environments. Although we might be able to specify proxies for those goals,
Goodhart's law suggests that some undesirable behaviour will score very well
according to these proxies, and therefore be reinforced in AIs trained on them
[Manheim and Garrabrant, 2018]. Even comparatively primitive systems to-
day demonstrate a range of specication gaming behaviours, some of which are
quite creative and unexpected, when we try to specify much simpler concepts
[Krakovna et al., 2020].
One way to address this problem is by incorporating human feedback into
the objective function used to evaluate AI behaviour during training. However,
there are at least three challenges to doing so. The rst is that it would be
prohibitively expensive for humans to provide feedback on all data required to
train AIs on complex tasks. This is known as the scalable oversight problem;
reward modelling19is the primary approach to addressing it. A second chal-
lenge is that, for long-term tasks, we might need to give feedback before we've
had the chance to see all the consequences of an agent's actions. Yet even in
domains as simple as Go, it's often very dicult to determine how good a given
19As in Christiano et al. [2017].
18
move is without seeing the game play out. And in larger domains, there may
be too many complex consequences for any single individual to evaluate. The
main approach to addressing this issue is by using multiple AIs to recursively
decompose the problem of evaluation, as in Irving et al. [2018]'s Debate, Leike
et al. [2018]'s Recursive Reward Modelling, and Christiano et al. [2018]'s Iter-
ated Amplication. By constructing superhuman evaluators, these techniques
also aim to address the third issue with human feedback: that humans can
be manipulated into interpreting behaviour more positively than they other-
wise would, for example by giving them misleading data (as in the robot hand
example from Christiano et al. [2017]).
Even if we solve outer alignment by specifying a \safe" objective function,
though, we may still encounter a failure of inner alignment : our agents might
develop goals which dier from the ones specied by that objective function.
This is likely to occur when the training environment contains subgoals which
are consistently useful for scoring highly on the given objective function, such as
gathering resources and information, or gaining power.20If agents reliably gain
higher reward after achieving such subgoals, then the optimiser might select
for agents which care about those subgoals for their own sake. (This is one
way agents might develop a nal goal of acquiring power, as mentioned at the
beginning of the section on Goals and Agency.)
This is analogous to what happened during the evolution of humans, when
we were \trained" by evolution to increase our genetic tness. In our ancestral
environment, subgoals like love, happiness and social status were useful for
achieving higher inclusive genetic tness, and so we evolved to care about them.
But now that we are powerful enough to reshape the natural world according
to our desires, there are signicant dierences between the behaviour which
would maximise genetic tness (e.g. frequent sperm or egg donation), and the
behaviour which we display in pursuit of the motivations we actually evolved.
Another example: suppose we reward an agent every time it correctly follows
a human instruction, so that the cognition which leads to this behaviour is
reinforced by its optimiser. Intuitively, we'd hope that the agent comes to have
the goal of obedience to humans. But it's also conceivable that the agent's
obedient behaviour is driven by the goal \don't get shut down", if the agent
understands that disobedience will get it shut down - in which case the optimiser
might actually reinforce the goal of survival every time it leads to a completed
instruction. So two agents, each motivated by one of these goals, might behave
very similarly until they are in a position to be disobedient without being shut
20Note the subtle distinction between the existence of useful subgoals, and my earlier dis-
cussion of the instrumental convergence thesis. The former is the claim that, for the specic
tasks on which we train AGIs, there are some subgoals which will be rewarded during train-
ing. The latter is the claim that, for most goals which an AGI might develop, there are some
specic subgoals which will be useful when the AGI tries to pursue those goals while deployed .
The latter implies the former only insofar as the convergent instrumental subgoals are both
possible and rewarded during training. Self-improvement is a convergent instrumental sub-
goal, but I don't expect most training environments to support it, and those that do may
have penalties to discourage it.
19
down.21
What will determine whether agents like the former or agents like the latter
are more likely to actually arise? As I mentioned above, one important factor is
whether there are subgoals which reliably lead to higher reward during training.
Another is how easy and benecial it is for the optimiser to make the agent
motivated by those subgoals, versus motivated by the objective function it's
being trained on. In the case of humans, for example, the concept of inclusive
genetic tness was a very dicult one for evolution to build into the human
motivational system. And even if our ancestors had somehow developed that
concept, they would have had diculty coming up with better ways to achieve
it than the ones evolution had already instilled in them. So in our ancestral
environment there was relatively little selection pressure for us to be inner-
aligned with evolution. In the context of training an AI, this means that the
complexity of the goals we try to instil in it incurs a double penalty: not only
does that complexity make it harder to specify an acceptable objective function,
it also makes that AI less likely to become motivated by our intended goals even
if the objective function is correct. Of course, late in training we expect our AIs
to have become intelligent enough that they'll understand exactly what goals
we intended to give them. But by that time their existing motivations may
be dicult to remove, and they'll likely also be intelligent enough to attempt
deceptive behaviour (as in the hypothetical example in the previous paragraph).
So how can we ensure inner alignment of AGIs with human intentions? This
research area has received less attention than outer alignment so far, because
it's a trickier problem to get a grip on. One potential approach involves adding
training examples where the behaviour of agents motivated by misaligned goals
diverges from that of aligned agents. Yet designing and creating this sort of
adversarial training data is currently much more dicult than mass-producing
data (e.g. via procedurally-generated simulations, or web scraping). This is
partly just because specic training data is harder to create in general, but also
for three additional reasons. Firstly, by default we simply won't know which
undesirable motivations our agents are developing, and therefore which ones to
focus on penalising. Interpretability techniques could help with this, but seem
very dicult to create (as I'll discuss further in the next section). Secondly, the
misaligned motivations which agents are most likely to acquire are those which
are most robustly useful. For example, it's particularly hard to design a training
environment where access to more information leads to lower reward. Thirdly,
we are most concerned about agents which have large-scale misaligned goals.
Yet large-scale scenarios are again the most dicult to set up during training,
21In fact these two examples showcase two dierent types of inner alignment failure: up-
stream mesa-optimisers anddownstream mesa-optimisers [Christiano, 2018b]. When trained
on a reward function R, upstream mesa-optimisers learn goals which lead to scoring highly on
R, or in other words are causally upstream of R. For example, humans learning to value nd-
ing food since it leads to greater reproductive success. Whereas downstream mesa-optimisers
learn goals that are causally downstream of scoring highly on R: for example, they learn the
goal of survival, and realise that if they score badly on R, they'll be discarded by the optimi-
sation procedure. This incentivises them to score highly on R, and hide their true goals - an
outcome called deceptive alignment [Hubinger et al., 2019].
20
either in simulation or in the real-world. So there's a lot of scope for more work
addressing these problems, or identifying new inner alignment techniques.
4.2 A more holistic view of alignment
Outer alignment is the problem of correctly evaluating AI behaviour; inner
alignment is the problem of making the AI's goals match those evaluations. To
some extent we can treat these as two separate problems; however, I think it's
also important to be aware of the ways in which the narrative of \alignment
= outer alignment + inner alignment" is incomplete or misleading. In partic-
ular, what would it even mean to implement a \safe" objective function? Is it
a function that we want the agent to actually maximise? Yet while maximis-
ing expected reward makes sense in formalisms like MDPs and POMDPs, it's
much less well-dened when the objective function is implemented in the real
world. If there's some sequence of actions which allows the agent to tamper
with the channel by which it's sent rewards, then \wireheading" by maxing out
that channel will practically always be the strategy which allows the agent to
receive the highest reward signal in the long term (even if the reward function
heavily penalises actions leading up to wireheading).22And if we use human
feedback, as previously discussed, then the optimal policy will be to manipu-
late or coerce the supervisors into giving maximally positive feedback. (There's
been some suggestion that \myopic" training could solve problems of tampering
and manipulation, but as I argued in Ngo [2020c], I expect that it merely hides
them.)
A second reason why reward functions are a \leaky abstraction" is that any
real-world agents we train in the foreseeable future will be very, very far away
from the limit of optimal behaviour on non-trivial reward functions. In particu-
lar, they will only see rewards for a tiny fraction of possible states. Furthermore,
if they're generalisation-based agents, they'll often perform new tasks after very
22One useful distinction here is between the message , the code, and the channel (following
Shannon). In the context of reinforcement learning, we can interpret the message to be
whatever goal is intended by the designers of the system (e.g. win at Starcraft); the code is
real numbers attached to states, with higher numbers indicating better states; and the channel
is the circuitry by which these numbers are passed to the agent. We have so far assumed that
the goal the agent learns is based on the message its optimiser infers from its reward function
(albeit perhaps in a way that generalises incorrectly, because it can be hard to decode the
intended message from a nite number of sampled rewards). But it's also possible that the
agent learns to care about the state of the channel itself. I consider pain in animals to be
one example of this: the message is that damage is being caused; the code is that more pain
implies more damage (as well as other subtleties of type and intensity); and the channel is the
neurons that carry those signals to our brains. In some cases, the code changes - for example,
when we receive an electric shock but know that it has no harmful eects. If we were only
concerned with the message, then we would ignore those cases, because they provide no new
content about damage to our body. Yet what actually happens is that we try to prevent those
signals being sent anyway, because we don't want to feel pain! Similarly, an agent which was
trained via a reward signal may desire to continue receiving those signals even when they no
longer carry the same message. Another way of describing this distinction is by contrasting
internalisation of a base objective versus modeling of that base objective, as discussed in
section 4 of Hubinger et al. [2019].
21
little training directly on those tasks. So the agent's behaviour in almost all
states will be primarily in
uenced not by the true value of the reward func-
tion on those states, but rather by how it generalises from previously-collected
data about other states.23This point is perhaps an obvious one, but it's worth
emphasising because there are so many theorems about the convergence of re-
inforcement learning algorithms which rely on visiting every state in the innite
limit, and therefore tell us very little about behaviour after a nite time period.
A third reason is that researchers already modify reward functions in ways
which change the optimal policy when it seems useful. For example, we add
shaping terms to provide an implicit curriculum, or exploration bonuses to push
the agent out of local optima. As a particularly safety-relevant example, neural
networks can be modied so that their loss on a task depends not just on
their outputs, but also on their internal representations [Ganin et al., 2016].
This is particularly useful for in
uencing how those networks generalise - for
example, making them ignore spurious correlations in the training data. But
again, it makes it harder to interpret reward functions as specications of desired
outcomes of a decision process.
How should we think about them instead? Well, in trying to ensure that
AGI will be aligned, we have a range of tools available to us - we can choose
the neural architectures, RL algorithms, environments, optimisers, etc, that are
used in the training procedure. We should think about our ability to specify an
objective function as the most powerful such tool. Yet it's not powerful because
the objective function denes an agent's motivations, but rather because samples
drawn from it shape that agent's motivations and cognition.
From this perspective, we should be less concerned about what the extreme
optima of our objective functions look like, because they won't ever come up dur-
ing training (and because they'd likely involve tampering). Instead, we should
focus on how objective functions, in conjunction with other parts of the train-
ing setup, create selection pressures towards agents which think in the ways
we want, and therefore have desirable motivations in a wide range of circum-
stances.24(See Arora [2019] for a more mathematical framing of a similar
point.)
This perspective provides another lens on the previous section's arguments
about AIs which are highly agentic. It's not the case that AIs will inevitably
end up thinking in terms of large-scale consequentialist goals, and our choice of
reward function just determines which goals they choose to maximise. Rather,
all the cognitive abilities of our AIs, including their motivational systems, will
23The mistake of thinking of RL agents solely as reward-maximisers (rather than having
other learned instincts and goals) has an interesting parallel in the history of the study of
animal cognition, where behaviorists focused on the ways that animals learned new behaviours
to increase reward, while ignoring innate aspects of their cognition.
24One useful example is the evolution of altruism in humans. While there's not yet any
consensus on the precise evolutionary mechanisms involved, it's notable that our altruistic
instincts extend well beyond the most straightforward cases of kin altruism and directly recip-
rocal altruism. In other words, some interaction between our direct evolutionary payos, and
our broader environment, led to the emergence of quite general altruistic instincts, making
humans \safer" (from the perspective of other species).
22
develop during training. The objective function (and the rest of the training
setup) will determine the extent of their agency and their attitude towards the
objective function itself! This might allow us to design training setups which
create pressures towards agents which are still very intelligent and capable of
carrying out complex tasks, but not very agentic - thereby preventing misalign-
ment without solving either outer alignment or inner alignment.
Failing that, though, we will need to align agentic AGIs. To do so, in addi-
tion to the techniques I've discussed above, we'll need to be able to talk more
precisely about what concepts and goals our agents possess. However, I am pes-
simistic about the usefulness of mathematics in making such high-level claims.
Mathematical frameworks often abstract away the aspects of a problem that
we actually care about, in order to make proofs easier - making those proofs
much less relevant than they seem. I think this criticism applies to the ex-
pected utility maximisation framework, as discussed previously; other examples
include most RL convergence proofs, and most proofs of robustness to adversar-
ial examples. Instead, I think we will need principles and frameworks similar to
those found in cognitive science and evolutionary biology. I think the categori-
sation of upstream vs downstream inner misalignment is an important example
of such progress;21I'd also like to see a framework in which we can talk sensibly
about gradient hacking,25and the distinction between being motivated by a re-
ward signal versus a reward function.22We should then judge reward functions
as \right" or \wrong" only to the extent that they succeed or fail in pushing
the agent towards developing desirable motivations and avoiding these sorts of
pathologies.
In the nal section, I will address the question of whether, if we fail, AGIs
with the goal of increasing their in
uence at the expense of humans will actually
succeed in doing so.
5 Control
It's important to note that my previous arguments by themselves do not imply
that AGIs will end up in control of the world instead of us. As an analogy,
scientic knowledge allows us to be much more capable than stone-age humans.
Yet if dropped back in that time with just our current knowledge, I very much
doubt that one modern human could take over the stone-age world. Rather, this
last step of the argument relies on additional predictions about the dynamics
of the transition from humans being the smartest agents on Earth to AGIs
taking over that role. These will depend on technological, economic and political
factors, as I'll discuss in this section. One recurring theme will be the importance
of our expectation that AGIs will be deployed as software that can be run on
many dierent computers, rather than being tied to a specic piece of hardware
25See Hubinger [2019a]: \Gradient hacking is a term I've been using recently to describe
the phenomenon wherein a deceptively aligned mesa-optimizer might be able to purposefully
act in ways which cause gradient descent to update it in a particular way."
23
as humans are.26
I'll start o by discussing two very high-level arguments. The rst is that
being more generally intelligent allows you to acquire more power, via large-
scale coordination and development of novel technological capabilities. Both of
these contributed to the human species taking control of the world; and they
both contributed to other big shifts in the distribution of power (such as the
industrial revolution). If the set of all humans and aligned AGIs is much less
capable in these two ways than the set of all misaligned AGIs, then we should
expect the latter to develop more novel technologies, and use them to amass
more resources, unless strong constraints are placed on them, or they're unable
to coordinate well (I'll discuss both possibilities shortly.)
On the other hand, though, it's also very hard to take over the world. In
particular, if people in power see their positions being eroded, it's generally
a safe bet that they'll take action to prevent that. Further, it's always much
easier to understand and reason about a problem when it's more concrete and
tangible; our track record at predicting large-scale future developments is pretty
bad. And so even if the high-level arguments laid out above seem dicult to
rebut, there may well be some solutions we missed which people will spot when
their incentives to do so, and the range of approaches available to them, are laid
out more clearly.
How can we move beyond these high-level arguments? In the rest of this
section I'll lay out two types of disaster scenarios, and then four factors which
will aect our ability to remain in control if we develop AGIs that are not fully
aligned:
1. Speed of AI development
2. Transparency of AI systems
3. Constrained deployment strategies
4. Human political and economic coordination
5.1 Disaster scenarios
There have been a number of attempts to describe the catastrophic outcomes
that might arise from misaligned superintelligences, although it has proven dif-
cult to characterise them in detail. Broadly speaking, the most compelling
scenarios fall into two categories. Christiano [2019] describes AGIs gaining in-

uence within our current economic and political systems by taking or being
given control of companies and institutions. Eventually \we reach the point
where we could not recover from a correlated automation failure" - after which
those AGIs are no longer incentivised to follow human laws. Hanson [2016]
also lays out a scenario in which virtual minds come to dominate the economy
(although he is less worried about misalignment, partly because he focuses on
26For an exploration of the possible consequences of software-based intelligence (as distinct
from the consequences of increased intelligence) see Hanson [2016].
24
emulated human minds). In both scenarios, biological humans lose in
uence
because they are less competitive at strategically important tasks, but no single
AGI is able to seize control of the world. To some extent these scenarios are
analogous to our current situation, in which large corporations and institutions
are able to amass power even when most humans disapprove of their goals. How-
ever, since these organisations are staed by humans, there are still pressures
on them to be aligned with human values which won't apply to groups of AGIs.
By contrast, Yudkowsky et al. [2008] and Bostrom [2014] describe scenarios
where a single AGI gains power primarily through technological breakthroughs,
in a way that's largely separate from the wider economy. The key assump-
tion which distinguishes this category of scenarios from the previous category
is that a single AGI will be able to gain enough power via such breakthroughs
that they can seize control of the world. Descriptions of these scenarios have
featured superhuman nanotechnology, biotechnology, and hacking; however, de-
tailed characterisations are dicult because the relevant technologies don't yet
exist. Yet it seems very likely that there exist some future technologies which
would provide a decisive strategic advantage if possessed only by a single actor,
and so the key factor in
uencing the plausibility of these scenarios is whether
AI development will be rapid enough to allow such concentration of power, as I
discuss below.
In either case, humans and aligned AIs end up with much less power than
misaligned AIs, which could then appropriate our resources towards their own
goals. An even worse scenario is if misaligned AGIs act in ways which are
deliberately hostile to human values - for example, by making threats to force
concessions from us [Clifton, 2020]. How can we avoid these scenarios? It's
tempting to aim directly towards the nal goal of being able to align arbitrarily
intelligent AIs, but I think that the most realistic time horizon to plan towards
is the point when AIs are much better than humans at doing safety research.
So our goal should be to ensure that those AIs are aligned, and that their safety
research will be used to build their successors. Which category of disaster is
most likely to prevent that depends not only on the intelligence, agency and
goals of the AIs we end up developing, but also on the four factors listed above,
which I'll explore in more detail now.
5.2 Speed of AI development
If AI development proceeds very quickly, then our ability to react appropriately
will be much lower. In particular, we should be interested in how long it will take
for AGIs to proceed from human-level intelligence to superintelligence, which
we'll call the takeo period . The history of systems like AlphaStar, AlphaGo
and OpenAI Five provides some evidence that this takeo period will be short:
after a long development period, each of them was able to improve rapidly from
top amateur level to superhuman performance. A similar phenomenon occurred
during human evolution, where it only took us a few million years to become
much more intelligent than chimpanzees. In our case one of the key factors was
scaling up our brain hardware - which, as I have already discussed, will be much
25
easier for AGIs than it was for humans.
While the question of what returns we will get from scaling up hardware
and training time is an important one, in the long term the most important
question is what returns we should expect from scaling up the intelligence of
scientic researchers - because eventually AGIs themselves will be doing the
vast majority of research in AI and related elds (in a process I've been calling
recursive improvement ). In particular, within the range of intelligence we're
interested in, will a given increase in the intelligence of an AGI increase the
intelligence of the best successor that AGI can develop by more than or less
than ? If more, then recursive improvement will eventually speed up the rate
of progress in AI research dramatically. In favour of this hypothesis, Yudkowsky
[2013] argues:
The history of hominid evolution to date shows that it has not re-
quired exponentially greater amounts of evolutionary optimization
to produce substantial real-world gains in cognitive performance - it
did not require ten times the evolutionary interval to go from Homo
erectus to Homo sapiens as from Australopithecus to Homo erectus.
All compound interest returned on discoveries such as the inven-
tion of agriculture, or the invention of science, or the invention of
computers, has occurred without any ability of humans to reinvest
technological dividends to increase their brain sizes, speed up their
neurons, or improve the low-level algorithms used by their neural
circuitry. Since an AI can reinvest the fruits of its intelligence in
larger brains, faster processing speeds, and improved low-level algo-
rithms, we should expect an AI's growth curves to be sharply above
human growth curves.
I consider this a strong argument that the pace of progress will eventually be-
come much faster than it currently is. I'm much less condent about when
the speedup will occur - for example, the positive feedback loop outlined above
might not make a big dierence until AGIs are already superintelligent, so that
the takeo period (as dened above) is still quite slow. There has been particu-
lar pushback against the more extreme fast takeo scenarios, which postulate a
discontinuous jump in AI capabilities before AI has had transformative impacts
[Christiano, 2018c, Grace, 2018c]. Some of the key arguments:
1. The development of AGI will be a competitive endeavour in which many
researchers will aim to build general cognitive capabilities into their AIs,
and will gradually improve at doing so. This makes it unlikely that there
will be low-hanging fruit which, when picked, allow large jumps in capa-
bilities. (Arguably, cultural evolution was this sort of low-hanging fruit
during human evolution, which would explain why it facilitated such rapid
progress.)
2. Compute availability, which on some views27is the key driver of progress
27See Sutton [2019].
26
in AI, increases fairly continuously.
3. Historically, continuous technological progress has been much more com-
mon than discontinuous progress [Grace, 2018b]. For example, progress on
chess-playing AIs was steady and predictable over many decades [Grace,
2018a].
Note that these three arguments are all consistent with AI development pro-
gressing continuously but at an increasing pace, as AI systems contribute to it
an increasing amount.
5.3 Transparency of AI systems
A transparent AI system is one whose thoughts and behaviour we can under-
stand and predict; we could be more condent that we can maintain control
over an AGI if it were transparent. If we could tell when a system is planning
treacherous behaviour, then we could shut it down before it gets the opportu-
nity to carry out that plan. Note that such information would also be valuable
for increasing human coordination towards dealing with AGIs; and of course for
training, as I discussed brie
y in the previous section.
Hubinger [2019b] lists three broad approaches to making AIs more trans-
parent. One is by creating interpretability tools which allow us to analyse the
internal functioning of an existing system. While our ability to interpret human
and animal brains is not currently very robust, this is partly because research
has been held back by the diculty of making high-resolution measurements.
By contrast, in neural networks we can read each weight and each activation
directly, as well as individually changing them to see what happens. On the
other hand, if our most advanced systems change rapidly, then previous trans-
parency research may quickly become obsolete. In this respect, neuroscientists
- who can study one brain architecture for decades - have it easier.
A second approach is to create training incentives towards transparency. For
example, we might reward an agent for explaining its thought processes, or for
behaving in predictable ways. Interestingly, ideas such as the cooperative eye
hypothesis imply that this occurred during human evolution, which suggests
that multi-agent interactions might be a useful way to create such incentives (if
we can nd a way to prevent incentives towards deception from also arising).
A third approach is to design algorithms and architectures that are inher-
ently more interpretable. For example, a model-based planner like AlphaGo
explores many possible branches of the game tree to decide which move to take.
By examining which moves it explores, we can understand what it's planning
before it chooses a move. However, in doing so we rely on the fact that Al-
phaGo uses an exact model of Go. More general agents in larger environments
will need to plan using compressed representations of those environments, which
will by default be much less interpretable. It also remains to be seen whether
transparency-friendly architectures and algorithms can be competitive with the
performance of more opaque alternatives, but I strongly suspect not.
27
Despite the diculties inherent in each of these approaches, one advantage
we do have in transparency analysis is access to dierent versions of an AI
over time. This mechanism of cross-examination in Debate takes advantage of
this [Barnes and Christiano, 2020]. Or as a more pragmatic example, if AI
systems which are slightly less intelligent than humans keep trying to deceive
their supervisors, that's pretty clear evidence that the more intelligent ones will
do so as well. However, this approach is limited because it doesn't allow us to
identify unsafe plans until they aect behaviour. If the realisation that treachery
is an option is always accompanied by the realisation that treachery won't work
yet, we might not observe behavioural warning signs until an AI arises which
expects its treachery to succeed.
5.4 Constrained deployment strategies
If we consider my earlier analogy of a modern human dropped in the stone age,
one key factor that would prevent them from taking over the world is that they
would be \deployed" in a very constrained way. They could only be in one place
at a time; they couldn't travel or even send messages very rapidly; they would
not be very robust to accidents; and there would be little existing infrastructure
for them to leverage. By contrast, it takes much more compute to train deep
learning systems than to run them - once an AGI has been trained, it will likely
be relatively cheap to deploy many copies of it. A misaligned superintelligence
with internet access will be able to create thousands of duplicates of itself, which
we will have no control over, by buying (or hacking) the necessary hardware.
At this point, our intuitions about the capabilities of a \single AGI" become
outdated, and the \second species" terminology becomes more appropriate.
We can imagine trying to avoid this scenario by deploying AGIs in more
constrained ways - for example by running them on secure hardware and only
allowing them to take certain pre-approved actions (such as providing answers to
questions) [Ngo, 2020e]. This seems signicantly safer. However, it also seems
less likely in a competitive marketplace - judging by today's trends, a more
plausible outcome is for almost everyone to have access to an AGI personal
assistant via their phone. This brings us to the fourth factor:
5.5 Human political and economic coordination
By default, we shouldn't rely on a high level of coordination to prevent AGI
safety problems. We haven't yet been able to coordinate adequately to prevent
global warming, which is a well-documented, gradually-worsening problem. In
the case of AGI deployment, the extrapolation from current behaviour to future
danger is much harder to model clearly. Meanwhile, in the absence of technical
solutions to safety problems, there will be strong short-term economic incentives
to ignore the lack of safety guarantees about speculative future events.
However, this is very dependent on the three previous points. It will be
much easier to build a consensus on how to deal with superintelligence if AI
systems approach then surpass human-level performance over a timeframe of
28
decades, rather than weeks or months. This is particularly true if less-capable
systems display misbehaviour which would clearly be catastrophic if performed
by more capable agents. Meanwhile, dierent actors who might be at the fore-
front of AGI development - governments, companies, nonprots - will vary in
their responsiveness to safety concerns, cooperativeness, and ability to imple-
ment constrained deployment strategies. And the more of them are involved,
the harder coordination between them will be.
6 Conclusion
Let's recap the second species argument as originally laid out, along with the
additional conclusions and clarications from the rest of the report.
1. We'll build AIs which are much more intelligent than humans; that is,
much better than humans at using generalisable cognitive skills to under-
stand the world .
2. Those AGIs will be autonomous agents which pursue long-term, large-
scale goals, because goal-directedness is reinforced in many training envi-
ronments, and because those goals will sometimes generalise to be larger
in scope.
3. Those goals will by default be misaligned with what we want, because our
desires are complex and nuanced, and our existing tools for shaping the
goals of AIs are inadequate.
4. The development of autonomous misaligned AGIs would lead to them
gaining control of humanity's future, via their superhuman intelligence,
technology and coordination - depending on the speed of AI development,
the transparency of AI systems, how constrained they are during deploy-
ment, and how well humans can cooperate politically and economically.
Personally, I am most condent in 1, then 4, then 3, then 2 (in each case
conditional on all the previous claims) - although I think there's room for rea-
sonable disagreement on all of them. In particular, the arguments I've made
about AGI goals might have been too reliant on anthropomorphism. Even if
this is a fair criticism, though, it's also very unclear how to reason about the
behaviour of generally intelligent systems without being anthropomorphic. The
main reason we expect the development of AGI to be a major event is because
the history of humanity tells us how important intelligence is. But it wasn't just
our intelligence that led to human success - it was also our relentless drive to
survive and thrive. Without that, we wouldn't have gotten anywhere. So when
trying to predict the impacts of AGIs, we can't avoid thinking about what will
lead them to choose some types of intelligent behaviour over others - in other
words, thinking about their motivations.
Note, however, that the second species argument, and the scenarios I've
outlined above, aren't meant to be comprehensive descriptions of all sources of
29
existential risk from AI. Even if the second species argument doesn't turn out to
be correct, AI will likely still be a transformative technology, and we should try
to minimise other potential harms. In addition to the standard misuse concerns
laid out in Brundage et al. [2018] (e.g. about AI being used to develop weapons),
we might also worry about increases in AI capabilities leading to undesirable
structural changes [Zwetsloot and Dafoe, 2019]. For example, they might shift
the oense-defence balance in cybersecurity [Garnkel and Dafoe, 2019], or lead
to more centralisation of human economic power. I consider Christiano [2019]'s
\going out with a whimper" scenario to also fall into this category. Yet there's
been little in-depth investigation of how structural changes might lead to long-
term harms, so I am inclined to not place much credence in such arguments
until they have been explored much more thoroughly.
By contrast, I think the AI takeover scenarios that this report focuses on
have received much more scrutiny - but still, as discussed previously, have big
question marks surrounding some of the key premises. However, it's important
to distinguish the question of how likely it is that the second species argument
is correct, from the question of how seriously we should take it. Often people
with very dierent perspectives on the latter actually don't disagree very much
on the former. I nd the following analogy from Stuart Russell illustrative:
suppose we got a message from space telling us that aliens would be landing on
Earth sometime in the next century. Even if there's doubt about the veracity
of the message, and there's doubt about whether the aliens will be hostile, we
(as a species) should clearly expect this event to be a huge deal if it happens,
and dedicate a lot of eort towards making it go well. In the case of AGI,
while there's reasonable doubt about what it will look like, its development
may nevertheless be the biggest thing that's ever happened. At the very least
we should put serious eort into understanding the arguments I've discussed
above, how strong they are, and what we might be able to do about them.28
7 Acknowledgements
This report beneted greatly from the contributions of a number of people.
In particular I'm grateful to Joe Carlsmith, Will MacAskill, Daniel Kokotajlo,
Rohin Shah, Vladimir Mikulik, Beth Barnes, and Ben Garnkel, for discussions
which shaped the ideas presented here. Thanks also to many others who gave
feedback (much of which can be found on the Alignment Forum).
28I want to explicitly warn against taking this argument too far, though - for example, by
claiming that AI safety work should still be a major priority even if the probability of AI
catastrophe is much less than 1%. This claim is misleading because most researchers in the
eld of safety think it's much higher than that; and also because, if it really is that low, there
are probably some fundamental confusions in our concepts and arguments that need to be
cleared up before we can actually start object-level work towards making AI safer.
30
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