Chimps, Humans, and AI: A Deceptive Analogy

The prospect of smarter-than-human artificial intelligence (AI) is often presented and thought of in terms of a simple analogy: AI will stand in relation to us the way we stand in relation to chimps. In other words, AI will be qualitatively more competent and powerful than us, and its actions will be as inscrutable to humans as current human endeavors (e.g. science and politics) are to chimps.

My aim in this essay is to show that this is in many ways a false analogy. The difference in understanding and technological competence found between modern humans and chimps is, in an important sense, a zero-to-one difference that cannot be repeated.

How are humans different from chimps?

A common answer to this question is that humans are smarter. Specifically, at the level of our individual cognitive abilities, humans, with our roughly three times larger brains, are just far more capable.

This claim no doubt contains a large grain of truth, as humans surely do beat chimps in a wide range of cognitive tasks. Yet it is also false in some respects. For example, chimps have superior working memory compared to humans, and apparently also beat humans in certain video games, including games involving navigation in complex mazes.

But researchers who study human uniqueness actually provide some rather different, more specific answers to this question. If we focus on individual mental differences in particular, researchers have found that, crudely speaking, humans are different from chimps in three principal ways: 1) we can learn language, 2) we have a strong orientation toward social learning, and 3) we are highly cooperative (among our ingroup, compared to chimps).

These differences have in turn resulted in two qualitative differences in the abilities of humans and chimps in today’s world.

I. Symbolic language

The first is that we humans have acquired an ability to think and communicate in terms of symbolic language that represents elaborate concepts. We can learn about the deep history of life and the universe, as well as the likely future of the universe, including the fundamental limits to future space travel and future computations. Any educated human can learn a good deal about these things whereas no chimp can.

Note how this is truly a zero-to-one difference: no symbolic language versus an elaborate symbolic language through which knowledge can be represented and continually developed (see chapter 1 in Deacon, 1997). It is the difference between having no science of physics versus having an elaborate such science with which we can predict future events and put hard limits on future possibilities.

This zero-to-one difference cannot really be repeated. Given that we already have physical models that predict, say, the future motion of planets and the solar system to a fairly high degree of accuracy, the best one can do in this respect is to (slightly) improve the accuracy of these predictions. Such further improvements cannot be compared to going from zero physics to current physics.

The same point applies to our scientific understanding more generally: we currently have theories that work decently well at explaining most of the phenomena around us. And though one can significantly improve the accuracy and sophistication of many of these theories, any such further improvement would be much less significant than the qualitative leap from absolutely no conceptual models to the entire collection of models and theories we currently have.

For example, going from no understanding of evolution by natural selection to the elaborate understanding of biology we have today cannot be matched, in terms of qualitative and revolutionary leaps, by further refinements in biology. We have already mapped out the core basics of biology (in fact a great deal more than that), and this can only be done once.

This is not an original point. Robin Hanson has made essentially the same point in response to the notion that future machines will be “as incomprehensible to us as we are to goldfish”:

This seems to me to ignore our rich multi-dimensional understanding of intelligence elaborated in our sciences of mind (computer science, AI, cognitive science, neuroscience, animal behavior, etc.).

… the ability of one mind to understand the general nature of another mind would seem mainly to depend on whether that first mind can understand abstractly at all, and on the depth and richness of its knowledge about minds in general. Goldfish do not understand us mainly because they seem incapable of any abstract comprehension. …

It seems to me that human cognition is general enough, and our sciences of mind mature enough, that we can understand much about quite a diverse zoo of possible minds, many of them much more capable than ourselves on many dimensions.

Ramez Naam has argued similarly in relation to the idea that there will be some future time or intelligence that we are fundamentally unable to understand. He argues that our understanding of the future is growing rather than shrinking as time progresses, and that AI and other future technologies will not be beyond comprehension:

All of those [future technologies] are still governed by the laws of physics. We can describe and model them through the tools of economics, game theory, evolutionary theory, and information theory. It may be that at some point humans or our descendants will have transformed the entire solar system into a living information processing entity — a Matrioshka Brain. We may have even done the same with the other hundred billion stars in our galaxy, or perhaps even spread to other galaxies.

Surely that is a scale beyond our ability to understand? Not particularly. I can use math to describe to you the limits on such an object, how much computing it would be able to do for the lifetime of the star it surrounded. I can describe the limit on the computing done by networks of multiple Matrioshka Brains by coming back to physics, and pointing out that there is a guaranteed latency in communication between stars, determined by the speed of light. I can turn to game theory and evolutionary theory to tell you that there will most likely be competition between different information patterns within such a computing entity, as its resources (however vast) are finite, and I can describe to you some of the dynamics of that competition and the existence of evolution, co-evolution, parasites, symbiotes, and other patterns we know exist.

Chimps cannot understand human politics and science to a similar extent. Thus, the truth is that there is a strong disanalogy between the understanding chimps have of humans versus the understanding that we humans — thanks to our conceptual tools — can have of any possible future intelligence (in physical and computational terms, say).

Note that the qualitative leap reviewed above was not one that happened shortly after human ancestors diverged from chimp ancestors. Instead, it was a much more recent leap that has been unfolding gradually since the first humans appeared, and which has continued to accelerate in recent centuries, as we have developed ever more advanced science and mathematics. In other words, this qualitative step has been a product of cultural evolution just as much as biological evolution. Early humans presumably had a roughly similar potential to learn modern language, science, mathematics, etc. But such conceptual tools could not be acquired in the absence of a surrounding culture able to teach these cultural innovations.

Ramez Naam has made a similar point:

If there was ever a singularity in human history, it occurred when humans evolved complex symbolic reasoning, which enabled language and eventually mathematics and science. Homo sapiens before this point would have been totally incapable of understanding our lives today. We have a far greater ability to understand what might happen at some point 10 million years in the future than they would to understand what would happen a few tens of thousands of years in the future.

II. Cumulative technological innovation

The second zero-to-one difference between humans and chimps is that we humans build things. Not just that we build things, but that we refine our technology over time. After all, many non-human animals use tools in the form of sticks and stones, and some even shape primitive tools of their own. But only humans improve and build upon the technological inventions of their ancestors.

Consequently, humans are unique in expanding their abilities by systematically exploiting their environment, molding the things around them into ever more useful self-extensions. We have turned wildlands into crop fields; we have created technologies that can harvest energy — from oil, gas, wind, and sun — and we have built external memories far more reliable than our own, such as books and hard discs.

This is another qualitative leap that cannot be repeated: the step from having absolutely no cumulative technology to exploiting and optimizing our external environment toward our own ends. The step from having no external memory to having the current repository of stored human knowledge at our fingertips, and from harvesting absolutely no energy (other than through individual digestion) to collectively harvesting and using hundreds of quintillions of Joules every year.

To be sure, it is possible to improve on and expand these innovations. We can harvest greater amounts of energy, for example, and create even larger external memories. Yet these are merely quantitative differences, and humanity indeed continually makes such improvements each year. They are not zero-to-one differences that only a new species could bring about. And what is more, we know that the potential for making further technological improvements is, at least in many respects, quite limited.

Take energy efficiency as an example. Many of our machines and energy harvesting technologies have already reached a significant fraction of the maximally possible efficiency. For example, electric motors and pumps tend to have around 90 percent energy efficiency, and the best solar panels have an efficiency greater than 40 percent. So as a matter of hard physical limits, many of our technologies cannot be made orders of magnitude more efficient; in fact, a large number of them can at most be marginally improved.

In sum, we are unique in being the first species that systematically sculpted our surrounding environment and turned it into ever-improving tools, many of which have near-maximal efficiency. This step cannot be repeated, only expanded further.


Just like the qualitative leap in our symbolic reasoning skills, the qualitative leap in our ability to create technology and shape our environment emerged, not between chimps and early humans, but between early humans and today’s humans, as the result of a cultural process occurring over thousands of years. In fact, the two leaps have been closely related: our ability to reason and communicate symbolically has enabled us to create cumulative technological innovation. Conversely, our technologies have allowed us to refine our knowledge and conceptual tools, by enabling us to explore and experiment, which in turn made us able to build even better technologies with which we could advance our knowledge even further, and so on.

This, in a nutshell, is the story of the growth of human knowledge and technology, a story of recursive self-improvement (see “On scientific networks” in Simler, 2019). It is not really a story about the individual human brain per se. After all, the human brain does not accomplish much in isolation (nor is it the brain with the largest number of neurons; several species have more neurons in the forebrain). It is more a story about what happened between and around brains: in the exchange of information in networks of brains and in the external creations designed by them. A story made possible by the fact that the human brain is unique in being by far the most cultural brain of all, with its singular capacity to learn from and cooperate with others.

The range of human abilities is surprisingly wide

Another way in which an analogy to chimps is frequently drawn is by imagining an intelligence scale along which different species are ranked, such that, for example, we have “rats at 30, chimps at 60, the village idiot at 90, the average human at 98, and Einstein at 100”, and where future AI may in turn be ranked many hundreds of points higher than Einstein. According to this picture, it is not just that humans will stand in relation to AI the way chimps stand in relation to humans, but that AI will be far superior still. The human-chimp analogy is, on this view, a severe understatement of the difference between humans and future AI.

Such an intelligence scale may seem intuitively compelling, but how does it correspond to reality? One way to probe this question is to examine the range of human abilities in chess. The standard way to rank chess skills is with the Elo rating system, which is a good predictor of the outcomes of chess games between different players, whether human, digital, or otherwise.

An early human beginner will have a rating around 300, a novice around 800, and a rating in the range 2000-2199 is ranked as “Expert”. The highest rating ever achieved is 2882 by Magnus Carlsen.

How large is this range of chess skills in an absolute sense? Remarkably large, it turns out. For example, it took more than four decades from when computers were first able to beat a human chess novice (the 1950s), until a computer was able to beat the best human player (1997, officially). In other words, the span from novice to Kasparov corresponded to more than four decades of progress in hardware — i.e. a million times more computing power — and software. This alone suggests that the human range of chess skills is rather wide.

Yet the range seems even broader when we consider the upper bounds of chess performance. After all, the fact that it took computers decades to go from human novice to world champion does not mean that the best human is not still ridiculously far from the best a computer could be in theory. Surprisingly, however, this latter distance does in fact seem quite small. Estimates suggest that the best possible chess machine would have an Elo rating around 3600, which means that the relative distance between the best possible computer and the best human is only around 700 Elo points (the Elo rating is essentially a measure of relative distance; 700 Elo points corresponds to a winning percentage of around 1.5 percent for the losing player).

This implies that the distance between the best human (Carlsen) and a chess “Expert” (someone belonging to the top 5 percent of chess players) is similar to the distance between the best human and the best possible chess brain, while the distance between a human beginner and the best human is far greater (2500 Elo points). This stands in stark contrast to the intelligence scale outlined above, which would predict the complete opposite: the distance from a human novice to the best human should be comparatively small whereas the distance from the best human to the optimal brain should be the larger one by far.


It may be objected that chess is a bad example, and that it does not really reflect what is meant by the intelligence scale above. But the question is then what would be a better measure. After all, a similar story seems to apply to other games, such as shogi and go: the human range of abilities is surprisingly wide and the best players are significantly closer to optimal than they are to novice players.

In fact, one can argue that the objection should go in the opposite direction, as human brains are not built for chess, and hence we should expect even the best humans to be far from optimal at it. We should expect to be much closer to “optimal” at solving problems that are more important for our survival, such as social cognition and natural language processing — skills that most people are wired to master at super-Carlsen levels.

Regardless, the truth is that humans are mastering ever more “games”, literal as well as figurative ones, at optimal or near-optimal levels. Not because evolution “just so happened to stumble upon the most efficient way to assemble matter into an intelligent system”, but rather because it created a species able to make cultural and technological progress toward ever greater levels of competence.

The cultural basis of the human capability expansion

The intelligence scale outlined above misses two key points. First, human abilities are not a constant. Whether we speak of individual abilities (e.g. the abilities of elite chess players) or humanity’s collective abilities (e.g. building laptops and sending people to the moon), it is clear that our abilities have increased dramatically as our culture and technology have expanded.

Second, because human abilities are not a constant, the range of human abilities is far wider, in an absolute sense, than the intelligence scale outlined above suggests, as it has grown and still continues to grow over time.

Chess is a good example of this. Untrained humans and chimps have the same (non-)skill level at chess. Yet thanks to culture, some people can learn to master the game. A wealthy society can allow people to specialize in chess, and makes it possible for knowledge to accumulate in books and experts. Eventually, it enables learning from super-human chess engines, whose innovations we can adopt just as we do those of other humans.

And yet we humans expand our abilities to a much greater extent than the example of increased human chess abilities suggests, as we not only expand our abilities by stimulating our brains with progressively better forms of practice and information, but also by extending ourselves directly with technology. For example, we can all use a chess engine to find great chess moves for us. Our latest technologies enable us to accomplish ever-more tasks that no human could ever accomplish unaided.

Worth noting in this regard is that this self-extension process seems to have slowed down in recent decades, likely because we have reaped most low-hanging fruits already, and in some respects because it is impossible to improve things much further (we already mentioned energy efficiency as an example where we are getting close to the upper limits in many respects).

This suggests that not only is there not a qualitative leap similar to that between chimps and modern humans ahead of us, but that even a quantitative growth explosion, with relative growth rates significantly higher than what we have seen in the past, should not be our default expectation either (for some support for this claim, see “Peak growth might lie in the past” in Vinding, 2017).

Why this is relevant

The errors of the human-chimp analogy are worth highlighting for a few reasons. First, the analogy can lead us to overestimate how much everything will change with AI. It leads us to expect qualitative leaps of sorts that cannot be repeated.

Second, the human-chimp analogy makes us underestimate how much we currently know and are able to understand. To think that intelligent systems of the future will be as incomprehensible to us today as human affairs are to chimps is to underestimate how extensive and universal our current knowledge of the world in fact is — not just when it comes to physical and computational limits, but also in relation to general economic and game-theoretic principles. We know a good deal about economic growth, for example, and this knowledge has a lot to say about how we should expect future intelligent systems to grow. In particular, it suggests that local AI-FOOM growth is unlikely.

The analogy can thus have an insidious influence by making us feel like current data and trends cannot be trusted much, because look how different humans are from chimps, and look how puny the human brain is compared to ultimate limits. I think this is exactly the wrong way to think about the future. We should base our expectations on a deep study of past trends, including the actual evolution of human competences — not simple analogies.

Relatedly, the human-chimp analogy is also relevant in that it can lead us to grossly overestimate the probability of an AI-FOOM scenario. That is, if we get the story about the evolution of human competences so wrong that we think the differences we observe today between chimps and modern humans reduce mostly to a story about changes in individual brains, then we are likely to have similarly inaccurate expectations about what comparable innovations in some individual machine are able to effect on their own.

If the human-chimp analogy leads us to (marginally) overestimate the probability of a FOOM scenario, it may nudge us toward focusing too much on some single, concentrated future thing that we expect to be all-important: the AI that suddenly becomes qualitatively more competent than humans. In effect, the human-chimp analogy can lead us to neglect broader factors, such as cultural and institutional developments.

Note that the above is by no means a case for complacency about risks from AI. It is important that we get a clear picture of such risks, and that we allocate our resources accordingly. But this requires us to rely on accurate models of the world. If we overemphasize one set of risks, we are by necessity underemphasizing others.

Suffering-Focused Ethics: Defense and Implications

The reduction of suffering deserves special priority. Many ethical views support this claim, yet so far these have not been presented in a single place. Suffering-Focused Ethics provides the most comprehensive presentation of suffering-focused arguments and views to date, including a moral realist case for minimizing extreme suffering. The book then explores the all-important issue of how we can best reduce suffering in practice, and outlines a coherent and pragmatic path forward.

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Suffering-Focused Ethics - 3D


“An inspiring book on the world’s most important issue. Magnus Vinding makes a compelling case for suffering-focused ethics. Highly recommended.”
— David Pearce, author of The Hedonistic Imperative and Can Biotechnology Abolish Suffering?

“We live in a haze, oblivious to the tremendous moral reality around us. I know of no philosopher who makes the case more resoundingly than Magnus Vinding. In radiantly clear and honest prose, he demonstrates the overwhelming ethical priority of preventing suffering. Among the book’s many powerful arguments, I would call attention to its examination of the overlapping biases that perpetuate moral unawareness. Suffering-Focused Ethics will change its readers, opening new moral and intellectual vistas. This could be the most important book you will ever read.
Jamie Mayerfeld, professor of political science at the University of Washington, author of Suffering and Moral Responsibility and The Promise of Human Rights

“In this important undertaking, Magnus Vinding methodically and convincingly argues for the overwhelming ethical importance of preventing and reducing suffering, especially of the most intense kind, and also shows the compatibility of this view with various mainstream ethical philosophies that don’t uniquely focus on suffering. His careful analytical style and comprehensive review of existing arguments make this book valuable reading for anyone who cares about what matters, or who wishes to better understand the strong rational underpinning of suffering-focused ethics.”
— Jonathan Leighton, founder of the Organisation for the Prevention of Intense Suffering, author of The Battle for Compassion: Ethics in an Apathetic Universe

“Magnus Vinding breaks the taboo: Today, the problem of suffering is the elephant in the room, because it is at the same time the most relevant and the most neglected topic at the logical interface between applied ethics, cognitive science, and the current philosophy of mind and consciousness. Nobody wants to go there. It is not good for your academic career. Only few of us have the intellectual honesty, the mental stamina, the philosophical sincerity, and the ethical earnestness to gaze into the abyss. After all, it might also gaze back into us. Magnus Vinding has what it takes. If you are looking for an entry point into the ethical landscape, if you are ready to face the philosophical relevance of extreme suffering, then this book is for you. It gives you all the information and the conceptual tools you need to develop your own approach. But are you ready?”
Thomas Metzinger, professor of philosophy at the Johannes Gutenberg University of Mainz, author of Being No One and The Ego Tunnel

Animal Advocates Should Focus On Antispeciesism, Not Veganism

First published: Dec. 2016.

How can we help nonhuman animals as much as possible? A good answer to this question could spare billions from suffering and death, while a bad one could condemn as many to that fate. So it’s worth taking our time to find good answers.

Focusing our advocacy on antispeciesism may be our best bet. In short, antispeciesist advocacy looks promising because it encompasses all nonhuman animals and implies great obligations toward them, and also because people may be especially receptive to such advocacy. More than that, antispeciesism is also likely to remain relevant for a long time, which makes it seem uniquely robust when we consider things from a very long-term perspective.

The value of antispeciesist advocacy

Antispeciesism addresses all the ways in which we discriminate against nonhuman animals, not just select sites of that discrimination, like circuses or food farms. Unlike more common approaches to animal advocacy, it demands that we take all forms of suffering endured by nonhuman animals into consideration.

Campaigns against fur farming, for instance, do not also cover the suffering and death involved in other forms of speciesist exploitation, such as the egg and dairy industries. Veganism, on the other hand, is much broader, in that it rejects all directly human-caused animal suffering. Advocating for the interests of comparatively few beings when we could advocate for the interests of many more with the same time and resources is likely a lost opportunity.

But even veganism is not as broad as antispeciesism, since it says nothing about the vast majority of sentient beings on the planet: animals who live in nature. Wild animals also suffer, and should not be granted less consideration simply because their suffering is not our fault.

Antispeciesism implies veganism – i.e. that we “exclude, as far as is possible and practicable, all forms of exploitation of, and cruelty to, animals for food, clothing or any other purpose” – but unlike veganism it also requires us to give serious consideration to nonhuman animals who are harmed in nature. Antispeciesism implies that we should help wild animals in need, just as we should help humans suffering from starvation or disease that we didn’t cause. Unfortunately, nonhuman animals are often harmed in nature, and often do succumb to starvation and thirst. Fortunately, there is much we can do to work for a future with fewer harms to them.

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Even if we expect people to be more receptive to messaging that is narrower in focus and easier to agree with, the all-encompassing nature of antispeciesist advocacy could mean it has greater value overall.

But are people really less receptive to such advocacy anyways? The concept of speciesism may seem abstract and advanced, and may strike us as something only committed animal rights advocates know of and understand. Yet there are reasons to think that this gut intuition is wrong.

Oscar Horta, a professor of moral philosophy who has delivered talks about animal rights around the world, has repeatedly put this pessimistic intuition to the test. At various talks delivered to Spanish high school students, he has attempted to systematically evaluate the attitudes of the attendees by giving them a questionnaire. One of the main results of this evaluation, according to Horta, was that “contrary to what some people think, most people who attended these talks accepted the arguments against speciesism.”[1]

So reportedly, a majority of attendees accepted the arguments against speciesism. And perhaps we should not be that surprised. Most people understand the concept of discrimination already, and speciesism is just another form of discrimination. The fact that many people are already familiar with the concept of discrimination and agree that it is not justified suggests that there might be a template upon which speciesism can easily be argued against. This could partly explain why most of Horta’s attendees accepted the arguments against speciesism. Another reason might be that the arguments against speciesism are exceptionally strong and hard to argue with.

Another interesting finding from Horta was that students appeared more receptive to a message opposing speciesism than to one supporting veganism. As he reports:

What is controversial is not really the discussion about speciesism. On the contrary, the most controversial point is (as might be expected), the discussion about whether we should stop eating animal “products”. Yet this discussion can also be carried out without major problems, at least if a couple of recommendations are followed: First of all, that this discussion arises not at the beginning of the talk, but rather towards the end, when speciesism and the need to respect all sentient beings has already been discussed. At that point, there is a greater willingness to consider this issue, because people who attend the talk then have a favorable attitude both toward animals and the speaker. But if we proceed in the opposite order and first argue for veganism and then raise the arguments about speciesism, the reaction is different. The result is that there is less willingness to consider the issue of veganism. And not only that, acceptance of arguments about speciesism is lower as well.

If this difference in effectiveness between vegan and antispeciesist messaging is similar in the broader public, the implications for advocacy are profound: even if our goal were only to promote veganism, the best way to do so might be to talk about speciesism, rather than, or at least before, talking about veganism. That talking about veganism straight away seems to have made the students less receptive not only to veganism itself but also to arguments against speciesism is also worth taking note of.

More thorough replication of Horta’s findings, on larger, more varied populations would significantly increase our confidence in the conclusion that antispeciesist advocacy is superior to vegan advocacy for creating antispeciesists, as well as vegans. Until then, Horta’s reported findings do at least suggest that people can accept arguments against speciesism.

Is vegan advocacy costly to wild animals?

Vegan advocacy could also be costly to animals not encompassed by vegan advocacy. Horta states:

There are many people involved in antispeciesism who are afraid to defend the idea that we should help animals in need in nature. Even though they fully agree with it, they believe that most people totally reject that idea, and even consider it absurd. However, among those attending the talks, there was a broad acceptance of the idea.

This is good news for animals and their advocates, given that the vast majority of nonhuman animals live in nature. Helping animals in the wild, such as through vaccinations and cures for diseases, may be among the most effective ways in which we can help nonhuman animals. Vegan advocacy excludes consideration of their interests, but antispeciesist advocacy does not.

This means that not only might it be costly to focus mainly on veganism in the interest of spreading veganism itself (compared to focusing mainly on speciesism and then raising the issue of veganism), but it might also be costly with respect to the goal of helping animals in nature. It’s possible that talking about veganism rather than speciesism makes it significantly harder to bring about interventions that could help nonhuman animals.

Compared to veganism, antispeciesism is also much harder to confuse with environmentalism, supporters of which often recommend overtly speciesist interventions such as the mass killing of beings in the name of “healthy ecosystems” and biodiversity. This lack of potential for confusion is another strong reason in favor of antispeciesist advocacy.

Beyond veganism

Antispeciesist advocacy is also much more neglected than vegan advocacy. Veganism is rising, and there are considerable incentives entirely separate from concern for nonhuman animals to move away from the production of animal “products”. In economic terms, it is inefficient to sustain an animal in order to use her flesh and skin rather than to grow meat and other animal-derived products directly, or replace them with plant-based alternatives. Similarly strong incentives exist in the realm of public health, which animal agriculture threatens by increasing the risks of zoonotic diseases, antibiotic resistant bacteria like MRSA, and cardiovascular disease. These incentives, none of which have anything to do with concern for nonhuman animals per se, could well be pushing humanity toward veganism more powerfully than anything else.

While veganism likely has a promising future, the future of antispeciesism seems much less clear and less promising, and has far fewer people working to promote it. This suggests that our own limited resources might be better spent promoting the latter. When thinking about how to build a better tomorrow, we should also consider the following tomorrows, and if we have a virtually vegan world a century from now due to the incentives mentioned above, the world will likely still be speciesist in many other respects. So in addition to the appeal antispeciesist advocacy has for the nonhuman animals whom humans are actively harming now, the explicitly antispeciesist approach is important for the sake of nonhuman animals in the future. Working towards a less speciesist future could both help close down the slaughterhouses, and help many animals long after.

Additionally, the spread of antispeciesism might also be a useful stepping stone toward concern for sentient beings of nonanimal kinds. Unfortunately, there is a risk that new kinds of sentient beings could emerge in the future – for instance, biologically engineered brains – and become the victims of a whole new kind of factory farming. Just like concern for humans who face discrimination can provide useful support today when the case against speciesism is made, antispeciesism could well be similarly generalizable and provide such support in the case against new forms of discrimination.

A final point in favor of antispeciesist advocacy over vegan advocacy is that the message of the former is clearly ethico-political in nature, and therefore does not risk being confused with an amoral consumerist preference or fad, as veganism often is. The core of antispeciesism is clear, easy to communicate, and much follows from it in terms of the practical implications.

Additional Resources

Oscar Horta provides more reasons to favor antispeciesist advocacy in a talk entitled “About Strategies”. See also my Notes on the Utility of Antispeciesist Advocacy.

This article was originally published on the website of Sentience Politics.


[1] My own machine-assisted translation

The future of growth: near-zero growth rates

First written: Jul. 2017; Last update: May 2020.

Exponential growth is a common pattern found throughout nature. Yet it is also a pattern that tends not to last, as growth rates tend to decline sooner or later.

In biology, this pattern of exponential growth that wanes off is found in everything from the development of individual bodies — for instance, in the growth of humans, which levels off in the late teenage years — to population sizes.

One may of course be skeptical that this general trend will also apply to the growth of our technology and economy at large, as innovation seems to continually postpone our clash with the ceiling, yet it seems inescapable that it must. For in light of what we know about physics, we can conclude that exponential growth of the kinds we see today, in technology in particular and in our economy more generally, must come to an end, and do so relatively soon.

Limits to growth

Physical limits to computation and Moore’s law

One reason we can make this assertion is that there are theoretical limits to computation. As physicist Seth Lloyd’s calculations show, a continuation of Moore’s law — in its most general formulation: “the amount of information that computers are capable of processing and the rate at which they process it doubles every two years” — would imply that we hit the theoretical limits of computation within 250 years:

If, as seems highly unlikely, it is possible to extrapolate the exponential progress of Moore’s law into the future, then it will only take two hundred and fifty years to make up the forty orders of magnitude in performance between current computers that perform 1010 operations per second on 1010 bits and our one kilogram ultimate laptop that performs 1051 operations per second on 1031 bits.

Similarly, physicists Lawrence Krauss and Glenn Starkman have calculated that, even if we factor in colonization of space at the speed of light, this doubling of processing power cannot continue for more than 600 years in any civilization:

Our estimate for the total information processing capability of any system in our Universe implies an ultimate limit on the processing capability of any system in the future, independent of its physical manifestation and implies that Moore’s Law cannot continue unabated for more than 600 years for any technological civilization.

In a more recent lecture and a subsequent interview, Krauss said that the absolute limit for the continuation of Moore’s law, in our case, would be reached in less than 400 years (the discrepancy — between the numbers 400 and 600 — is at least in part because Moore’s law, in its most general formulation, has played out for more than a century in our civilization at this point). And, as both Krauss and Lloyd have stressed, these are ultimate theoretical limits, resting on assumptions that are unlikely to be met in practice, such as expansion at the speed of light. What is possible, in terms of how long Moore’s law can continue for, given both engineering and economic constraints is likely significantly less. Indeed, we are already close to approaching the physical limits of the paradigm that Moore’s law has been riding on for more than 50 years — silicon transistors, the only paradigm that Gordon Moore was talking about originally — and it is not clear whether other paradigms will be able to take over and keep the trend going.

Limits to the growth of energy use

Physicist Tom Murphy has calculated a similar limit for the growth of the energy consumption of our civilization. Based on the observation that the energy consumption of the United States has increased fairly consistently with an average annual growth rate of 2.9 percent over the last 350 odd years (although the growth rate appears to have slowed down in recent times and been stably below 2.9 since c. 1980), Murphy proceeds to derive the limits for the continuation of similar energy growth. He does this, however, by assuming an annual growth rate of “only” 2.3 percent, which conveniently results in an increase of the total energy consumption by a factor of ten every 100 years. If we assume that we will continue expanding our energy use at this rate by covering Earth with solar panels, this would, on Murphy’s calculations, imply that we will have to cover all of Earth’s land with solar panels in less than 350 years, and all of Earth, including the oceans, in 400 years.

Beyond that, assuming that we could capture all of the energy from the sun by surrounding it in solar panels, the 2.3 percent growth rate would come to an end within 1,350 years from now. And if we go further out still, to capture the energy emitted from all the stars in our galaxy, we get that this growth rate must hit the ceiling and become near-zero within 2,500 years (of course, the limit of the physically possible must be hit earlier, indeed more than 500 years earlier, as we cannot traverse our 100,000 light year-wide Milky Way in only 2,500 years).

One may suggest that alternative sources of energy might change this analysis significantly, yet, as Murphy notes, this does not seem to be the case:

Some readers may be bothered by the foregoing focus on solar/stellar energy. If we’re dreaming big, let’s forget the wimpy solar energy constraints and adopt fusion. The abundance of deuterium in ordinary water would allow us to have a seemingly inexhaustible source of energy right here on Earth. We won’t go into a detailed analysis of this path, because we don’t have to. The merciless growth illustrated above means that in 1400 years from now, any source of energy we harness would have to outshine the sun.

Essentially, keeping up the annual growth rate of 2.3 percent by harnessing energy from matter not found in stars would force us to make such matter hotter than stars themselves. We would have to create new stars of sorts, and, even if we assume that the energy required to create such stars is less than the energy gained, such an endeavor would quickly run into limits as well. For according to one estimate, the total mass of the Milky Way, including dark matter, is only 20 times greater than the mass of its stars. Assuming a 5:1 ratio of dark matter to ordinary matter, this implies that that there is only about 3.3 times as much ordinary non-stellar matter as there is stellar matter in our galaxy. Thus, even if we could convert all this matter into stars without spending any energy and harvest the resulting energy, this would only give us about 50 years more of keeping up with the annual growth rate of 2.3 percent.1

Limits derived from economic considerations

Similar conclusions to the ones drawn above for computation and energy also seem to follow from calculations of a more economic nature. For, as economist Robin Hanson has argued, projecting present economic growth rates into the future also leads to a clash against fundamental limits:

Today we have about ten billion people with an average income about twenty times subsistence level, and the world economy doubles roughly every fifteen years. If that growth rate continued for ten thousand years[,] the total growth factor would be 10200.

There are roughly 1057 atoms in our solar system, and about 1070 atoms in our galaxy, which holds most of the mass within a million light years. So even if we had access to all the matter within a million light years, to grow by a factor of 10200each atom would on average have to support an economy equivalent to 10140 people at today’s standard of living, or one person with a standard of living 10140 times higher, or some mix of these.

Indeed, current growth rates would “only” have to continue for three thousand years before each atom in our galaxy would have to support an economy equivalent to a single person living at today’s living standard, which already seems rather implausible (not least because we can only access a tiny fraction of “all the matter within a million light years” in three thousand years). Hanson does not, however, expect the current growth rate to remain constant, but instead, based on the history of growth rates, expects a new growth mode where the world economy doubles within 15 days rather than 15 years:

If a new growth transition were to be similar to the last few, in terms of the number of doublings and the increase in the growth rate, then the remarkable consistency in the previous transitions allows a remarkably precise prediction. A new growth mode should arise sometime within about the next seven industry mode doublings (i.e., the next seventy years) and give a new wealth doubling time of between seven and sixteen days.

And given this more than a hundred times greater growth rate, the net growth that would take 10,000 years to accomplish given our current growth rate (cf. Hanson’s calculation above) would now take less than a century to reach, while growth otherwise requiring 3,000 years would require less than 30 years. So if Hanson is right, and we will see such a shift within the next seventy years, what seems to follow is that we will reach the limits of economic growth, or at least reach near-zero growth rates, within a century or two. Such a projection is also consistent with the physically derived limits of the continuation of Moore’s law; not that economic growth and Moore’s law are remotely the same, yet they are no doubt closely connected: economic growth is largely powered by technological progress, of which Moore’s law has been a considerable subset in recent times.

The conclusion we reach by projecting past growth trends in computing power, energy, and the economy is the same: our current growth rates cannot go on forever. In fact, they will have to decline to near-zero levels very soon on a cosmic timescale. Given the physical limits to computation, and hence, ultimately, to economic growth, we can conclude that we must be close to the point where peak relative growth in our economy and our ability to process information occurs — that is, the point where this growth rate is the highest in the entire history of our civilization, past and future.

Peak growth might lie in the past

This is not, however, to say that this point of maximum relative growth necessarily lies in the future. Indeed, in light of the declining economic growth rates we have seen over the last few decades, it cannot be ruled out that we are now already past the point of “peak economic growth” in the history of our civilization, with the highest growth rates having occurred around 1960-1980, cf. these declining growth rates and this essay by physicist Theodore Modis. This is not to say that we most likely are, yet it seems that the probability that we are is non-trivial.

A relevant data point here is that the global economy has seen three doublings since 1965, where the annual growth rate was around six percent, and yet the annual growth rate today is only a little over half — around 3.5 percent — of, and lies stably below, what it was those three doublings ago. In the entire history of economic growth, this seems unprecedented, suggesting that we may already be on the other side of the highest growth rates we will ever see. For up until this point, a three-time doubling of the economy has, rare fluctuations aside, led to an increase in the annual growth rate.

And this “past peak growth” hypothesis looks even stronger if we look at 1955, with a growth rate of a little less than six percent and a world product at 5,430 billion 1990 U.S dollars, which doubled four times gives just under 87,000 billion — about where we should expect today’s world product to be. Yet throughout the history of our economic development, four doublings has meant a clear increase in the annual growth rate, at least in terms of the underlying trend; not a stable decrease of almost 50 percent. This tentatively suggests that we should not expect to see growth rates significantly higher than those of today sustained in the future.

A hypothetical model: roughly symmetric growth rates

If we assume a model of the growth of the global economy where the annual growth rate is roughly symmetrical around the time the growth rate was at its global maximum, and then assume that this global maximum occurred around 1965, this means that we should expect the annual growth rate three doublings earlier, c. 1900, to be the same as the annual growth rate three doublings later, c. 2012. What do we observe? Three doublings earlier it was around 2.5 percent, while it was around 3.5 percent three doublings later, at least according to one source (although other sources actually do put the number at around 2.5 percent). Not a clear match, nor a clear falsification.

Yet if we look at the growth rates of advanced economies around 2012, we find that the growth rate is actually significantly lower than 2.5 percent, namely 1.2-2.0 percent. And given that less developed economies are expected to grow significantly faster than more developed ones, as the more advanced economies have paved the way and made high-hanging fruits more accessible, the (already not so big) 2.5 vs. 3.5 percent mismatch could be due to this gradually diminishing catch-up effect. Indeed, if we compare advanced economies today with advanced economies c. 1900, we find that the growth rate was significantly higher back then,3 suggesting that the symmetrical model may in fact overestimate current and future growth if we look only at advanced economies.4

Could we be past peak growth in science and technology?

That peak growth lies in the past may also be true of technological progress in particular, or at least many forms of technological progress, including the progress in computing power tracked by Moore’s law, where the growth rate appears to have been highest around 1990-2005, and to since have been in decline, cf. this article and the first graphs found here and here. Similarly, various sources of data and proxies tracking the number of scientific articles published and references cited over time also suggest that we could be past peak growth in science as well, at least in many fields when evaluated based on such metrics, with peak growth seeming to have been reached around 2000-2010.

Yet again, these numbers — those tracking economic, technological, and scientific progress — are of course closely connected, as growth in each of these respects contributes to, and is even part of, growth in the others. Indeed, one study found the doubling time of the total number of scientific articles in recent decades to be 15 years, corresponding to an annual growth rate of 4.7 percent, strikingly similar to the growth rate of the global economy in recent decades. Thus, declining growth rates both in our economy, technology, and science cannot be considered wholly independent sources of evidence that growth rates are now declining for good. We can by no means rule out that growth rates might increase in all these areas in the future — although, as we saw above with respect to the limits of Moore’s law and economic progress, such an increase, if it is going to happen, must be imminent if current growth rates remain relatively stable.

Absolute and relative growth

The economic “peak growth” discussed above relates to relative growth, not absolute growth. These are worth distinguishing. For in terms of absolute growth, annual growth is significantly higher today than it was in the 1960s, where the greatest relative growth to date occurred. The global economy grew with about half a trillion 1990 US dollars each year in the sixties, whereas it grows with about two trillion now. So in this absolute sense, we are seeing significantly more growth today than we did 50 years ago, although we now have significantly lower growth rates.

If we assume the model with symmetric growth rates mentioned above and make a simple extrapolation based on it, what follows is that our time is also a special one when it comes to absolute annual growth. The picture we get is the following (based on an estimate of past growth rates from economic historian James DeLong):

Year                             World GDP
(in trillions)           
Annual
growth rate           
Absolute annual
growth (in trillions)
920   0.032      0.13        0.00004
1540   0.065      0.25        0.0002
1750   0.13      0.5        0.0007
1830   0.27      1        0.003
1875  0.55      1.8        0.01
1900   1.1      2.5        0.03
1931   2.3      3.8        0.09
1952   4.6      4.9        0.2
1965   9.1      5.9        0.5
1980   18      4.4        0.8
1997   36      4.0        1.4
2012   72      3.5        2.1

 

Predicted values given roughly symmetric growth rates around 1965 (mirroring growth rates above):

2037 144 1.8 2.6
2082 288 1 2.9
2162 576 0.5 2.9
2372 1152 0.25 2.9
2992 2304 0.13 3.0

 

We see that the absolute annual growth in GDP seems to follow an s-curve with an inflection point right about today, as we see that the period from 1997 to 2012 saw the biggest jump in absolute annual growth in a doubling ever; an increase of 0.7 trillion, from 1.4 to 2.1.

It is worth noting that economist Robert Gordon predicts similar growth rates as the model above over the next few decades, as do various other estimates of the future of economic growth by economists. In contrast, engineer Paul Daugherty and economist Mark Purdy predict higher growth rates due to the effects of AI on the economy, yet the annual growth rates they predict in 2035 are still only around three percent for most of the developed economies they looked at, roughly at the same level as the current growth rate of the global economy. On a related note, economist William Nordhaus has attempted to make an economic analysis of whether we are approaching an economic singularity, in which he concludes, based on various growth models, that we do not appear to be, although he does not rule out that an economic singularity, i.e. significantly faster economic growth, might happen eventually.

Might recent trends make us bias-prone?

How might it be relevant that we may be past peak economic growth at this point? Could it mean that our expectations for the future are likely to be biased? Looking back toward the 1960s might be instructive in this regard. For when we look at our economic history up until the 1960s, it is not so strange that people made many unrealistic predictions about the future around this period. Because not only might it have appeared natural to project the high growth rate at the time to remain constant into the future, which would have led to today’s global GDP being more than twice of what it is; it might also have seemed reasonable to predict the growth rates to keep on rising even further. After all, that was what they had been doing consistently up until that point, so why should it not continue in the following decades, resulting in flying cars and conversing robots by the year 2000? Such expectations were not that unreasonable given the preceding economic trends.

The question is whether we might be similarly overoptimistic about future economic progress today given recent, possibly unique, growth trends, specifically the unprecedented increase in absolute annual growth that we have seen over the past two decades — cf. the increase of 0.7 trillion mentioned above. The same may apply to the trends in scientific and technological progress cited above, where peak growth in many areas appears to have happened in the period 1990-2010, meaning that we could now be at a point where we are disposed to being overoptimistic about further progress.

Yet, again, it is highly uncertain at this point whether growth rates, of the economy in general and of progress in technology and science in particular, will increase again in the future. Future economic growth may not conform well to the model with roughly symmetric growth rates around the 1960s, although the model certainly deserves some weight. All we can say for sure is that growth rates must become near-zero relatively soon. What the path toward that point will look like remains an open question. We could well be in the midst of a temporary decline in growth rates that will be followed by growth rates significantly greater than those of the 1960s, cf. the new growth mode envisioned by Robin Hanson.5

Implications: this is an extremely special time

Applying the mediocrity principle, we should not expect to live in an extremely unique time. Yet, in light of the facts about the ultimate limits to growth seen above, it is clear that we do: we are living during the childhood of civilization where there is still rapid growth, at the pace of doublings within a couple of decades. If civilization persists with similar growth rates, it will soon become a grown-up with near-zero relative growth. And it will then look back at our time — today plus minus a couple of centuries, most likely — as the one where growth rates were by far the highest in its entire history, which may be more than a trillion years.

It seems that a few things follow from this. First, more than just being the time where growth rates are the highest, this may also, for that very reason, be the time where individuals can influence the future of civilization more than any other time. In other words, this may be the time where the outcome of the future is most sensitive to small changes, as it seems plausible, although far from clear, that small changes in the trajectory of civilization are most significant when growth rates are highest. An apt analogy might be a psychedelic balloon with fluctuating patterns on its surface, where the fluctuations that happen to occur when we blow up the balloon will then also be blown up and leave their mark in a way that fluctuations occurring before and after this critical growth period will not (just like quantum fluctuations in the early universe got blown up during cosmic expansion, and thereby in large part determined the grosser structure of the universe today). Similarly, it seems much more difficult to cause changes across all of civilization when it spans countless star systems compared to today.

That being said, it is not obvious that small changes — in our actions, say — are more significant in this period where growth rates are many orders of magnitude higher than in any other time. It could also be that such changes are more consequential when the absolute growth is the highest. Or perhaps when it is smallest, at least as we go backwards in time, as there were far fewer people back when growth rates were orders of magnitude lower than today, and hence any given individual comprised a much greater fraction of all individuals than an individual does today.

Still, we may well find ourselves in a period where we are uniquely positioned to make irreversible changes that will echo down throughout the entire future of civilization.6 To the extent that we are, this should arguably lead us to update toward trying to influence the far future rather than the near future. More than that, if it does hold true that the time where the greatest growth rates occur is indeed the time where small changes are most consequential, this suggests that we should increase our credence in the simulation hypothesis. For if realistic sentient simulations of the past become feasible at some point, the period where the future trajectory of civilization seems the most up for grabs would seem an especially relevant one to simulate and learn more about. However, one can also argue that the sheer historical uniqueness of our current growth rates alone, regardless of whether this is a time where the fate of our civilization is especially volatile, should lead us to increase this credence, as such uniqueness may make it a more interesting time to simulate, and because being in a special time in general should lead us to increase our credence in the simulation hypothesis (see for instance this talk for a case for why being in a special time makes the simulation hypothesis more likely).7

On the other hand, one could also argue that imminent near-zero growth rates, along with the weak indications that we may now be past peak growth in many respects, provide a reason to lower our credence in the simulation hypothesis, as these observations suggest that the ceiling for what will be feasible in the future may be lower than we naively expect in light of today’s high growth rates. And thus, one could argue, it should make us more skeptical of the central premise of the simulation hypothesis: that there will be (many) ancestor simulations in the future. To me, the consideration in favor of increased credence seems stronger, although it does not significantly move my overall credence in the hypothesis, as there are countless other factors to consider.8


Appendix: Questioning our assumptions

Caspar Oesterheld pointed out to me that it might be worth meditating on how confident we can be in these conclusions given that apparently solid predictions concerning the ultimate limits to growth have been made before, yet quite a few of these turned out to be wrong. Should we not be open to the possibility that the same might be true of (at least some of) the limits we reviewed in the beginning of this essay?

Could our understanding of physics be wrong?

One crucial difference to note is that these failed predictions were based on a set of assumptions — e.g. about the amount of natural resources and food that would be available — that seem far more questionable than the assumptions that go into the physics-based predictions we have reviewed here: that our apparently well-established physical laws and measurements indeed are valid, or at least roughly so. The epistemic status of this assumption seems a lot more solid, to put it mildly. So there does seem to be a crucial difference here. This is not to say, however, that we should not maintain some degree of doubt as to whether this assumption is correct (I would argue that we always should). It just seems that this degree of doubt should be quite low.

Yet, to continue the analogy above, what went wrong with the aforementioned predictions was not so much that limits did not exist, but rather that humans found ways of circumventing them through innovation. Could the same perhaps be the case here? Could we perhaps some day find ways of deriving energy from dark energy or some other yet unknown source, even though physicists seem skeptical? Or could we, as Ray Kurzweil speculates, access more matter and energy by finding ways of travelling faster than light, or by finding ways of accessing other parts of our notional multiverse? Might we even become able to create entirely new ones? Or to eventually rewrite the laws of nature as we please? (Perhaps by manipulating our notional simulators?) Again, I do not think any of these possibilities can be ruled out completely. Indeed, some physicists argue that the creation of new pocket universes might be possible, not in spite of “known” physical principles (or rather theories that most physicists seem to believe, such as inflationary theory), but as a consequence of them. However, it is not clear that anything from our world would be able to expand into, or derive anything from, the newly created worlds on any of these models (which of course does not mean that we should not worry about the emergence of such worlds, or the fate of other “worlds” that we perhaps could access).

All in all, the speculative possibilities raised above seem unlikely, yet they cannot be ruled out for sure. The limits we have reviewed here thus represent a best estimate given our current, admittedly incomplete, understanding of the universe in which we find ourselves, not an absolute guarantee. However, it should be noted that this uncertainty cuts both ways, in that the estimates we have reviewed could also overestimate the limits to various forms of growth by countless orders of magnitude.

Might our economic reasoning be wrong?

Less speculatively, I think, one can also question the validity of our considerations about the limits of economic progress. I argued that it seems implausible that we in three thousand years could have an economy so big that each atom in our galaxy would have to support an economy equivalent to a single person living at today’s living standard. Yet could one not argue that the size of the economy need not depend on matter in this direct way, and that it might instead depend on the possible representations that can be instantiated in matter? If economic value could be mediated by the possible permutations of matter, our argument about a single atom’s need to support entire economies might not have the force it appears to have. For instance, there are far more legal positions on a Go board than there are atoms in the visible universe, and that’s just legal positions on a Go board. Perhaps we need to be more careful when thinking about how atoms might be able to create and represent economic value?

It seems like there is a decent point here. Still, I think economic growth at current rates is doomed. First, it seems reasonable to be highly skeptical of the notion that mere potential states could have any real economic value. Today at least, what we value and pay for is not such “permutation potential”, but the actual state of things, which is as true of the digital realm as of the physical. We buy and stream digital files such as songs and movies because of the actual states of these files, while their potential states mean nothing to us. And even when we invest in something we think has great potential, like a start-up, the value we expect to be realized is still ultimately one that derives from its actual state, namely the actual state we hope it will assume, not its number of theoretically possible permutations.

It is not clear why this would change, or how it could. After all, the number of ways one can put all the atoms in the galaxy together is the same today as it will be ten thousand years from now. Organizing all these atoms into a single galactic supercomputer would only seem to increase the value of their actual state.

Second, economic growth still seems tightly constrained by the shackles of physical limitations. For it seems inescapable that economies, of any kind, are ultimately dependent on the transfer of resources, whether these take the form of information or concrete atoms. And such transfers require access to energy, the growth of which we know to be constrained, as is true of the growth of our ability to process information. As these underlying resources that constitute the lifeblood of any economy stop growing, it seems unlikely that the economy can avoid this fate as well. (Tom Murphy touches on similar questions in his analysis of the limits to economic growth.)

Again, we of course cannot exclude that something crucial might be missing from these considerations. Yet the conclusion that economic growth rates will decline to near-zero levels relatively soon, on a cosmic timescale at least, still seems a safe bet in my view.

Acknowledgments

I would like to thank Brian Tomasik, Caspar Oesterheld, Duncan Wilson, Kaj Sotala, Lukas Gloor, Magnus Dam, Max Daniel, and Tobias Baumann for valuable comments and inputs. This essay was originally published at the website of the Foundational Research Institute, now the Center on Long-Term Risk. 


Notes

1. One may wonder whether there might not be more efficient ways to derive energy from the non-stellar matter in our galaxy than to convert it into stars as we know them. I don’t know, yet a friend of mine who does research in plasma physics and fusion says that he does not think one could, especially if we, as we have done here, disregard the energy required to clump the dispersed matter together so as to “build” the star, a process that may well take more energy than the star can eventually deliver.

The aforementioned paper by Lawrence Krauss and Glenn Starkman also contains much information about the limits of energy use, and in fact uses accessible energy as the limiting factor that bounds the amount of information processing any (local) civilization could do (they assume that the energy that is harvested is beamed back to a “central observer”).

2. And I suspect many people who have read about “singularity”-related ideas are overconfident, perhaps in part due to the comforting narrative and self-assured style of Ray Kurzweil, and perhaps due to wishful thinking about technological progress more generally.

3. According to one textbook “Outside the European world, per capita incomes stayed virtually constant from 1700 to about 1950 […]” implying that the global growth rate in 1900 was raised by the most developed economies, and they must thus have had a growth rate greater than 2.5 percent.

4. A big problem with this model is that it is already pretty much falsified by the data, at least when it comes to “pretty”, as opposed to approximate, symmetry. For given symmetry in the growth rates around 1965, the time it takes for three doublings to occur should be the same in either direction, whereas the data shows that this is not the case — 65 years minus 47 years equals 18 years, which is roughly a doubling. One may be able to correct this discrepancy a tiny bit by moving the year of peak growth a bit further back, yet this cannot save the model. This lack of actual symmetry should reduce our credence in the symmetric model as a description of the underlying pattern of our economic growth, yet I do not think it fully discredits it. Rough symmetry still seems a decent first approximation to past growth rates, and deviations may in part be explainable by factors such as the high, yet relatively fast diminishing, contribution to growth from developing economies.

5. It should be noted, though, that Hanson by no means rules out that such a growth mode may never occur, and that we might already be past, or in the midst of, peak economic growth: “[…] it is certainly possible that the economy is approaching fundamental limits to economic growth rates or levels, so that no faster modes are possible […]”

6. The degree to which there is sensitivity to changes of course varies between different endeavors. For instance, natural science seems more convergent than moral philosophy, and thus its development is arguably less sensitive to the particular ideas of individuals working on it than the development of moral philosophy is.

7. One may then argue that this should lead us to update toward focusing more on the near future. This may be true. Yet should we update more toward focusing on the far future given our ostensibly unique position to influence it? Or should we update more toward focusing on the near future given increased credence in the simulation hypothesis? (Provided that we indeed do increase this credence, cf. the counter-consideration above.) In short, it mostly depends on the specific probabilities we assign to these possibilities. I myself happen to think the far future should dominate, as I assign the simulation hypothesis (as commonly conceived) a very small probability.

8. For instance, fundamental epistemological issues concerning how much one can infer based on impressions from a simulated world (which may only be your single mind) about a simulating one (e.g. do notions such as “time” and “memory” correspond to anything, or even make sense, in such a “world”?); the fact that the past cannot be simulated realistically, since we can only have incomplete information about a given physical state in the past (not only because we have no way to uncover all the relevant information, but also because we cannot possibly represent it all, even if we somehow could access it — for instance, we cannot faithfully represent the state of every atom in our solar system in any point in the past, as this would require too much information), and a simulation of the past that contains incomplete information would depart radically from how the actual past unfolded, as all of it has a non-negligible causal impact (even single photons, which, it appears, are detectable by the human eye), and this is especially true given that the vast majority of information would have to be excluded (both due to practical constraints to what can be recovered and what can be represented); whether conscious minds can exist on different levels of abstraction; etc.

Free Will: Emphasizing Possibilities

I suspect the crux of discussions and worries about (the absence of) “free will” is the issue of possibilities. I also think it is a key source of confusion. Different people are talking about possibilities in different senses without being clear about it, which leads them to talk past each other, and perhaps even to confuse and dispirit laypeople by making them feel they have no possibilities in any sense whatsoever.

Different Emphases

Thinkers who take different positions on free will tend to emphasize different things. One camp tends to say “we don’t have free will, since all our actions are caused by prior causes that are ultimately beyond our own control, and in this there are no ‘alternative possibilities'”.

Another camp, so-called compatibilists, will tend to agree with the latter point about prior causes, but they choose to emphasize possibilities: “complex agents can act within a range of possibilities in a way crude objects like rocks cannot, and such agents truly do weigh and choose between these options”.

In essence, what I think the latter camp is emphasizing is the fact that we have ex-ante possibilities: a range of possibilities we can choose from in expectation. (For example, in a game of chess, your ex-ante possibilities are comprised by the set of moves allowed by the rules of the game.) And since this latter camp defines free will roughly as the ability to make choices among such ex-ante possibilities, they conclude that we indeed do have free will.

I doubt any philosopher arguing against the existence of free will would deny the claim that we have ex-ante possibilities. After all, we all conceive of various possibilities in our minds that we weigh and choose between, and we indeed cannot talk meaningfully about ethics, or choices in general, without such a framework of ex-ante possibilities. (Whether possibilities exist in any other sense than ex ante, and whether this is ethically relevant, are separate questions.)

Given the apparent agreement on these two core points — 1) our actions are caused by prior causes, and 2) we have ex-ante possibilities — the difference between the two camps mostly seems to lie in how they define free will and whether they prefer to emphasize 1) or 2).

The “Right” Definition of Free Will

People in these two camps will often insist that their definition of free will is the one that matches what most people mean by free will. I think both camps are right and wrong about this. I think it is misguided to think that most people have anything close to a clear definition of free will in their minds, as opposed to having a jumbled network of associations that relate to a wide range of notions, including notions of independence from prior causes and notions of ex-ante possibilities.

Experimental philosophy indeed also hints at a much more nuanced picture of people’s intuitions and conceptions of “free will”, and reveals them to be quite unclear and conflicting, as one would expect.

Emphasizing Both

I believe the two distinct emphases outlined above are both important yet insufficient on their ownThe emphasis on prior causes is important for understanding the nature of our choices and actions. In particular, it helps us understand that our choices do not comprise a break with physical mechanism, but that they are indeed the product of complex such mechanisms (which include the mechanisms of our knowledge and intentions, as well as the mechanism of weighing various ex-ante possibilities).

In turn, this emphasis may help free us from certain bad ideas about human choices, such as naive ideas about how anyone can always pull themselves up by their bootstraps. It may also help us construct better incentives and institutions based on an actual understanding of the mechanism of our choices rather than supernatural ideas about them. Lastly, it may help us become more understanding toward others, such as by reminding us that we cannot reasonably expect people to act on knowledge they do not possess.

Similarly, emphasizing our ex-ante possibilities is important for our ability to make good decisions. Mistakingly believing that one has only one possibility, ex ante, rather than thinking through all possibilities will likely lead to highly sub-optimal outcomes, whether it be in a game of chess or a major life decision. Aiming to choose the ex-ante possibility that seems best in expectation is crucial for us to make good choices. Indeed, this is what good decision-making is all about.

More than that, an emphasis on ex-ante possibilities can also help instill in us the healthy and realistic versions of bootstrap-pulling attitudes, namely that hard work and dedication indeed are worthwhile and truly can lead us in better directions.

Both Emphases Have Pitfalls (in Isolation)

Our minds intuitively draw inferences and associations based on the things we hear. When it comes to “free will”, I suspect most of us have quite leaky conceptual networks, in that the distinct clusters of sentiments we intuitively tie to the term “free will” readily cross-pollute each other — a form of sentiment synesthesia.

So when someone says “we don’t have free will, everything is caused by prior causes”, many people may naturally interpret this as implying “we don’t have ex-ante possibilities, and so we cannot meaningfully think in terms of alternative possibilities”, even though this does not follow. This may in turn lead to bad decisions and feelings of disempowerment. It may also lead people to think that it makes no sense to punish people, or that we cannot meaningfully say things like “you really should have made a better choice”. Yet these things do make sense. They serve to create incentives by making a promise for the future — “people who act like this will pay a price” — which in turn nudges people toward some of their ex-ante possibilities over others.

More than that, a naive emphasis on the causal origins of our actions may also lead people to think that certain feelings — such as pride, regret, and hatred — are always unreasonable and should never be entertained. Yet this does not follow either. Indeed, these feelings likely have great utility in some circumstances, even if such circumstances are rare.

A similar source of confusion is to say that our causal nature implies that everything is just a matter of luck. Although this is true in some ultimate sense, in another sense — the everyday sense that distinguishes between things won through hard effort versus dumb luck — everything is obviously not just a matter of luck. And I suspect most people’s intuitive associations can also be leaky between these very different notions of “luck”. Consequently, unreserved claims about everything being a matter of luck also risk having unfortunate effects, such as leading us to underemphasize the importance of effort.

Such pitfalls also exist relative to the claim “you could not have done otherwise”. For what we often mean by this claim, when we talk about specific events in everyday conversations, is that “this event would have happened even if you had done things differently” (that is: the environment constrained you, and your efforts were immaterial). This is very different from saying, for example, “you could not have done otherwise because your deepest values compelled you” (meaning: the environment may well have allowed alternative possibilities, but your values did not). The latter is often true of our actions, yet it is in many ways the very opposite of what we usually mean by “you could not have done otherwise”.

Hence, confusion is likely to emerge if someone simply declares “you could not have done otherwise” about all actions without qualification. And such confusion may well persist even in the face of explicit qualifications, since confusions deep down at the intuitive level may not be readily undone by just a few cerebral remarks.

Conversely, there are also pitfalls of sentiment leakiness in the opposite direction. When someone says “ex-ante possibilities are real, and they play a crucial role in our decision-making”, people may naturally interpret this as implying “our actions are not caused by prior causes, and this is crucial for our decision-making”. And this may in turn lead to the above-mentioned mistakes that the prior-causes emphasis can help us avoid: misunderstanding our mechanistic nature and failing to act on such an understanding, as well as entertaining unreasonable ideas about how we can expect people to act.

 

This is why one has to be careful in one’s communication about “free will”, and to clearly flag these non sequiturs. “We are caused by prior causes” does not mean “we have no ex-ante possibilities”, and conversely, “we have ex-ante possibilities” does not imply “we are not caused by prior causes”.

 


Acknowledgments: Thanks to Mikkel Vinding for comments.

On Insects and Lexicality

Many people, myself included, find it plausible that suffering of a certain intensity, such as torture, carries greater moral significance than any amount of mild suffering. One may be tempted to think that views of this kind imply we should primarily prioritize the beings most likely to experience these “lexically worse” states of suffering (LWS) — presumably beings with large brains.* By extension, one may think such views will generally imply little priority to beings with small, less complex brains, such as insects. (Which is probably also a view we would intuitively like to embrace, given the inconvenience of the alternative.) 

Yet while perhaps intuitive, I do not think this conclusion follows. The main argument against it, in my view, is that we should maintain a non-trivial probability that beings with small brains, such as insects, indeed can experience LWS (regardless of how we define these states). After all, on what grounds can we confidently maintain they cannot?

And if we then assume an expected value framework, and multiply the large number of insects by a non-trivial probability of them being able to experience LWS, we find that, in terms of presently existing beings, the largest amount of LWS in expectation may well be found in small beings such as insects.


* It should be noted in this context, though, that many humans ostensibly cannot feel (at least physical) pain, whereas many beings with smaller brains show every sign of having this capacity, which suggests brain size is a poor proxy for the ability to experience pain, let alone the ability to experience LWS, and that genetic variation in certain pain-modulating genes may well be a more important factor.


More literature

On insects:

The Importance of Insect Suffering

Do Bugs Feel Pain?

How to Avoid Hurting Insects

The Moral Importance of Invertebrates Such as Insects

On Lexicality:

Value Lexicality

Many-valued logic as a reply to sequence arguments in value theory

Physics Is Also Qualia

In this post, I seek to clarify what I consider to be some common confusions about consciousness and “physics” stemming from a failure to distinguish clearly between ontological and epistemological senses of “physics”.

Clarifying Terms

Two senses of the word “physics” are worth distinguishing. There is physics in an ontological sense: roughly speaking, the spatio-temporal(-seeming) world that in many ways conforms well to our best physical theories. And then there is physics in an epistemological sense: a certain class of models we have of this world, the science of physics.

“Physics” in this latter, epistemological sense can be further divided into 1) the physical models we have in our minds, versus 2) the models we have external to our minds, such as in our physics textbooks and computer simulations. Yet it is worth noting that, to the extent we ourselves have any knowledge of the models in our books and simulations, we only have this knowledge by representing it in our minds. Thus, ultimately, all the knowledge of physical models we have, as subjects, is knowledge of the first kind: as appearances in our minds.*

In light of these very different senses of the term “physics”, it is clear that the claim that “physics is also qualia” can be understood in two very different ways: 1) in the sense that the physical world, in the ontological sense, is qualia, or “phenomenal”, and 2) that our models of physics are qualia, i.e. that our models of physics are certain patterns of consciousness. The first of these two claims is surely the most controversial one, and I shall not defend it here; I explore it here and here.

Instead, I shall here focus on the latter claim. My aim is not really to defend it, as I already briefly did that above: all the knowledge of physics we have, as subjects, ultimately appears as experiential patterns in our minds. (Although talk of the phenomenology of, say, operations in Hilbert spaces admittedly is rare.) I take this to be obvious, and hit an impasse with anyone who disagrees. My aim here is rather to clarify some confusions that arise due to a lack of clarity about this, and due to conflations of the two senses of “physics” described above.

The Problem of Reduction: Epistemological or Ontological?

I find it worth quoting the following excerpt from a Big Think interview with Sam Harris. Not because there is anything atypical about what Harris says, but rather because I think he here clearly illustrates the prevailing lack of clarity about the distinction between epistemology and ontology in relation to “the physical”.

If there’s an experiential internal qualitative dimension to any physical system then that is consciousness. And we can’t reduce the experiential side to talk of information processing and neurotransmitters and states of the brain […]. Someone like Francis Crick said famously you’re nothing but a pack of neurons. And that misses the fact that half of the reality we’re talking about is the qualitative experiential side. So when you’re trying to study human consciousness, for instance, by looking at states of the brain, all you can do is correlate experiential changes with changes in brain states. But no matter how tight these correlations become that never gives you license to throw out the first person experiential side. That would be analogous to saying that if you just flipped a coin long enough you would realize it had only one side. And now it’s true you can be committed to talking about just one side. You can say that heads being up is just a case of tails being down. But that doesn’t actually reduce one side of reality to the other.

Especially worth resting on here is the statement “half of the reality we’re talking about is the qualitative experiential side.” Yet is this “half of reality” an “ontological half” or an “epistemological half”? That is, is there a half of reality out there that is part phenomenal, and part “non-phenomenal” — perhaps “inertly physical”? Or are we rather talking about two different phenomenal descriptions of the same thing, respectively 1) physico-mathematical models of the mind-brain (and these models, again, are also qualia, i.e. patterns of consciousness), and 2) all other phenomenal descriptions, i.e. those drawing on the countless other experiential modalities we can currently conceive of — emotions, sounds, colors, etc. — as well as those we can’t? I suggest we are really talking about two different descriptions of the same thing.

A similar question can be raised in relation to Harris’ claim that we cannot “reduce one side of reality to the other.” Is the reduction in question, or rather failure of reduction, an ontological or an epistemological one? If it is ontological, then it is unclear what this means. Is it that one side of reality cannot “be” the other? This does not appear to be Harris’ view, even if he does tacitly buy into ontologically distinct sides (as opposed to descriptions) of reality in the first place.

Yet if the failure of reduction is epistemological, then there is in fact little unusual about it, as failures of epistemological reduction, or reductions from one model to another, are found everywhere in science. In the abstract sciences, for example, one axiomatic system does not necessarily reduce to another; indeed, we can readily create different axiomatic systems that not only fail to reduce to each other yet which actively contradict each other. And hence we cannot derive all of mathematics, broadly construed, from a single axiomatic system.

Similarly, in the empirical sciences, economics does not “reduce to” quantum physics. One may object that economics does reduce to quantum physics in principle, yet it should then be noted that 1) the term “in principle” does an enormous amount of work here, arguably about as much as it would have to do in the claim that “quantum physics can explain consciousness in principle” — after all, physics and economics invoke very different models and experiential modalities (economic theories are often qualitative in nature, and some prominent economists have even argued they are primarily so). And 2) a serious case can be made against the claim that even all the basic laws found in chemistry, the closest neighbor of physics, can be derived from fundamental physical theories, even in principle (see e.g. Berofsky, 2012, chap. 8). This case does not rest on there being something mysterious going on between our transition from theories of physics to theories of chemistry, nor that new fundamental forces are implicated, but merely that our models in these respective fields contain elements not reducible, even in principle, to our models in other areas.

Thus, at the level of our minds, we can clearly construct many different mental models which we cannot reduce to each other, even in principle. Yet this merely says something about our models and epistemology. It hardly comprises a deep metaphysical mystery.

Denying the Reality of Consciousness

The fact that the world conforms, at least roughly, to description in “physical” terms seems to have led some people to deny that consciousness in general exists. Yet this, I submit, is a fallacy: the fact that we can model the world in one set of terms which describe certain of its properties does not imply that we cannot describe it in another set of terms that describe other properties truly there as well, even if we cannot derive one from the other.

By analogy, consider again physics and economics: we can take the exact same object of study — say, a human society — and describe aspects of it in physical terms (with models of thermodynamics, classical mechanics, electrodynamics, etc.), yet we cannot from any such description or set of descriptions meaningfully derive a description of the economics of this society. It would clearly be a fallacy to suggest that this implies facts of economics cannot exist.

Again, I think the confusion derives from conflating epistemology with ontology: “physics”, in the epistemological sense of “descriptions of the world in physico-mathematical terms”, appears to encompass “everything out there”, and hence, the reasoning goes, nothing else can exist out there. Of course, in one sense, this is true: if a description in physico-mathematical terms exhaustively describes everything out there, then there is indeed nothing more to be said about it — in physico-mathematical terms. Yet this says nothing about the properties of what is out there in other terms, as illustrated by the economics example above. (Another reason some people seem to deny the reality of consciousness, distinct from conflation of the epistemological and the ontological, is “denial due to fuzziness”, which I have addressed here.)

This relates, I think, to the fundamental Kantian insight on epistemology: we never experience the world “out there” directly, only our own models of it. And the fact that our physical model of the world — including, say, a physical model of the mind-brain of one’s best friend — does not entail other phenomenal modalities, such as emotions, by no means implies that the real, ontological object out there which our physical model reflects, such as our friend’s actual mind-brain, does not instantiate these things. That would be to confuse the map with the territory. (Our emotional model of our best friend does, of course, entail emotions, and it would be just as much of a fallacy to say that, since such emotional models say nothing about brains in physical terms, descriptions of the latter kind have no validity.)

Denials of this sort can have serious ethical consequences, not least since the most relevant aspects of consciousness, including suffering, fall outside descriptions of the world in purely physical terms. Thus, if we insist that only such physico-mathematical descriptions truly describe the world, we seem forced to conclude that suffering, along with everything else that plausibly has moral significance, does not truly exist. Which, in turn, can keep us from working toward a sophisticated understanding of these things, and from creating a better world accordingly.

 


* And for this reason, the answer to the question “how do you know you are conscious?” will ultimately be the same as the answer to the question “how do you know physics (i.e. physical models) exist?” — we experience these facts directly.

Thinking of Consciousness as Waves

First written: Dec 14, 2018, Last update: Jan 2, 2019.

 

How can we think about the relationship between the conscious and the physical? In this essay I wish to propose a way of thinking about it that might be fruitful and surprisingly intuitive, namely to think of consciousness as waves.

The idea is quite simple: one kind of conscious experience corresponds to, or rather conforms to description in terms of, one kind of wave. And by combining different kinds of waves, we can obtain an experience with many different properties in one.

It should be noted that I in this post merely refer to waves in an abstract sense to illustrate a general point. That is, I do not refer to electromagnetic waves in particular (as some theories of consciousness do), nor to quantum waves (as other theories do), nor to any other particular kind of wave (such as Selen Atasoy’s so-called connectome-specific harmonic waves*). The point here is not what kind of wave, or indeed which physical state in general, that mediates different states of consciousness. The point is merely to devise a metaphor that can render intuitive the seemingly unintuitive, namely: how can we get something complex and multifaceted from something very simple without having anything seemingly spooky or strange, such as strong emergence, in between? In particular, how can we say that brains mediate conscious experience without saying that, say, electrons mediate conscious experience? I believe thinking about consciousness in terms of waves can help dissolve this confusion. 

The magic of waves is that we can produce (or to an arbitrary level of precision approximate) any kind of complex, multifaceted wave by adding simple sine waves together.

 

Image result for waves sine
Sine waves with different frequencies.

 

In this way, it is possible, for instance, to decompose any recorded song — itself a complex, multifaceted wave — into simple, tedious-sounding sine waves. Each resulting sine wave can be said to comprise an aspect of the song, yet not in any recognizable way. The whole song is in fact a sum of such waves, not in a strange way that implies strong emergence, but merely in a complicated, composite way.

Another way to think about waves that can help us think more clearly about emergent complexity is to think of a wave that is very small in both amplitude and duration. If this were a sound wave, it would be an extremely short-lived, extremely low-volume sound. On a visual representation of an entire song file, this sound would look more akin to a dot than a wave.

 

Image result for a point math
A dot.

 

And such simple sound waves can also be put together so as to create a song (for instance, one can take the sine waves obtained by decomposing a song and then chop them into smaller bits and decrease their amplitude). It will just, to make a song, take a very great number of such small waves superimposed (if the song is to be loud enough to hear) and in succession (if the song is to last for more than a split-second).

 

The deeper point here is that waves are waves, no matter how small or simple, large or complex. Yet not all waves comprise what we would recognize as music. Similarly, even if all physical states are phenomenal in the broadest sense, this does not imply that they are conscious in the sense of being an ordered, multifaceted whole. Unfortunately, we do not as yet have good, analogous terms for “sound” and “music” in the phenomenal realm — perhaps we could use “phenomenality” and “consciousness”, respectively?

The problem is indeed that we are limited by language, in that the word “conscious” usually only connotes an ordered, composite mind rather than the property of phenomenality in the most general sense. Consequently, if we think all that exists is either music or non-sound, metaphorically speaking, we are bound to be confused. But if we instead expand our vocabulary, and thereby expand our allowed ways of thinking, our confusion can, I think, be readily dissolved. If we think of the phenomenality of the simplest physical systems as being nothing like consciousness in the usual sense of a composite mind but rather as a state of hyper-crude phenomenality — i.e. “phenomenal noise” that is nothing like a song but more akin to a low, short-lived sound, and yet unimaginably more crude still — then the problem of consciousness, as commonly (mis)conceived, seems to become a lot less confusing.**

Avoiding Confusion Due to Fuzziness

A more specific point of confusion the wave metaphor can help us dissolve is the notion that consciousness is so fuzzy a category that it in fact does not really exist, just like tables and chairs do not really exist. As I have argued elsewhere, I think this is a non sequitur. The fact that the categories of tables and chairs are themselves fuzzy does not imply that the physical properties of the objects to which we refer with these labels are inexact, let alone non-existent. The objects have the physical properties they have regardless of how we label them. Or, to continue the analogy to waves above, and songs in particular: although there is ambiguity about what counts as a song, this does not imply that we cannot speak in precise, factual terms about the properties of a given song — for instance, whether a given song contains a 440 Hz tone.

Similarly, the fact that consciousness, as in “an ordered, composite mind”, is a fuzzy category (after all, what counts as ordered? Do psychotic states? Fleeting dreams?) does not imply that any given phenomenal state we refer to with this term does not have exact and clearly identifiable phenomenal properties — e.g. an experience of the color red or the sensation of fear; properties that exist regardless of how outside observers choose to label them.

And although our labels for categorizing particular phenomenal states themselves tend to be fuzzy to some extent — e.g. which part of the spectrum below counts as red? — this does not imply that we cannot distinguish between different states, nor that we cannot draw any clear boundaries. For instance, we can clearly distinguish between the blue and the red zones respectively on the illustration below despite its gradation.

 

Image result for range of color
A linear representation of the visible light spectrum with wavelengths in nanometers.

 

Just as we can point toward a confined range of wavelengths which induce an experience of (some kind of) red in most people upon hitting their retinas, we can also, in principle, point to a range of physical states that mediate specific phenomenal states. This includes the phenomenal states we call suffering, with the fuzziness of what counts as suffering contained within and near the bounds of this range, while the physical states outside this range, especially those far away, do not mediate suffering, cf. the non-red range in the illustration above.

Thus, by analogy to how we can have precise descriptions of the properties of a song, even as an exact definition of what counts as a song escapes us, there is no reason why we should not be able to speak in factual and precise terms about the phenomenal aspects of a mind and its physical signatures, including the “red range” of wavelengths that comprise phenomenal suffering, metaphorically speaking. And a sophisticated understanding of this notional range is indeed of paramount importance for the project of reducing suffering.


* Note that these seemingly different kinds of waves and theories of consciousness can be identical, since connectome-specific harmonic waves could turn out to be coherent waves in the electromagnetic quantum field, as would seem suggested by a hypothesis known as quantum brain dynamics (I do not necessarily endorse this particular hypothesis).

** Another useful analogy for thinking more clearly about the seemingly crazy notion that “everything is conscious” — or rather: phenomenal — is to think about the question, Is everything light? For in a highly non-standard sense, everything is indeed “light”, in that electromagnetic waves permeate the universe in the form of cosmic background radiation, although everything is not permeated by light in the usual sense of visible electromagnetic radiation (wavelengths around 400–700 nm). We may thus think of consciousness as analogous to visible light (they can also both be more or less intense and have various nuances), and electromagnetic radiation as analogous to phenomenality — the more general phenomenon that encompasses the specific one.

 

Is AI Alignment Possible?

The problem of AI alignment is usually defined roughly as the problem of making powerful artificial intelligence do what we humans want it to do. My aim in this essay is to argue that this problem is less well-defined than many people seem to think, and to argue that it is indeed impossible to “solve” with any precision, not merely in practice but in principle.

There are two basic problems for AI alignment as commonly conceived. The first is that human values are non-unique. Indeed, in many respects, there is more disagreement about values than people tend to realize. The second problem is that even if we were to zoom in on the preferences of a single human, there is, I will argue, no way to instantiate a person’s preferences in a machine so as to make it act as this person would have preferred.

Problem I: Human Values Are Non-Unique

The common conception of the AI alignment problem is something like the following: we have a set of human preferences, X, which we must, somehow (and this is usually considered the really hard part), map onto some machine’s goal function, Y, via a map f, let’s say, such that X and Y are in some sense isomorphic. At least, this is a way of thinking about it that roughly tracks what people are trying to do.

Speaking in these terms, much attention is being devoted to Y and f compared to X. My argument in this essay is that we are deeply confused about the nature of X, and hence confused about AI alignment.

The first point of confusion is about the values of humanity as a whole. It is usually acknowledged that human values are fuzzy, and that there are some disagreements over values among humans. Yet it is rarely acknowledged just how strong this disagreement in fact is.

For example, concerning the ideal size of the future population of sentient beings, the disagreement is near-total, as some (e.g. some defenders of the so-called Asymmetry in population ethics, as well as anti-natalists such as David Benatar) argue that the future population should ideally be zero, while others, including many classical utilitarians, argue that the future population should ideally be very large. Many similar examples could be given of strong disagreements concerning the most fundamental and consequential of ethical issues, including whether any positive good can ever outweigh extreme suffering. And on many of these crucial disagreements, a very large number of people will be found on both sides.

Different answers to ethical questions of this sort do not merely give rise to small practical disagreements; in many cases, they imply completely opposite practical implications. This is not a matter of human values being fuzzy, but a matter of them being sharply, irreconcilably inconsistent. And hence there is no way to map the totality of human preferences, “X”, onto a single, well-defined goal-function in a way that does not conflict strongly with the values of a significant fraction of humanity. This is a trivial point, and yet most talk of human-aligned AI seems oblivious to this fact.

Problem II: Present Human Preferences Are Underdetermined Relative to Future Actions

The second problem and point of confusion with respect to the nature of human preferences is that, even if we focus only on the present preferences of a single human, then these in fact do not, and indeed could not possibly, determine with much precision what kind of world this person would prefer to bring about in the future.

This claim requires some unpacking, but one way to realize what I am trying to say here is to think in terms of the information required to represent the world around us. A precise such representation would require an enormous amount of information, indeed far more information than what can be contained in our brain. This holds true even if we only consider morally relevant entities around us — on the planet, say. There are just too many of them for us to have a precise representation of them. By extension, there are also too many of them for us to be able to have precise preferences about their individual states. Given that we have very limited information at our disposal, all we can do is express extremely coarse-grained and compressed preferences about what state the world around us should ideally have. In other words: any given human’s preferences are bound to be extremely vague about the exact ideal state of the world right now, and there will be countless moral dilemmas occurring across the world right now to which our preferences, in their present state, do not specify a unique solution.

And yet this is just considering the present state of the world. When we consider future states, the problem of specifying ideal states and resolutions to hitherto unknown moral dilemmas only explodes in complexity, and indeed explodes exponentially as time progresses. It is simply a fact, and indeed quite an obvious one at that, that no single brain could possibly contain enough information to specify unique, or indeed just qualified, solutions to all moral dilemmas that will arrive in the future. So what, then, could AI alignment relative to even a single brain possibly mean? How can we specify Y with respect to these future dilemmas when X itself does not specify solutions?

We can, of course, try to guess what a given human, or we ourselves, might say if confronted with a particular future moral dilemma and given knowledge about it, yet the problem is that our extrapolated guess is bound to be just that: a highly imperfect guess. For even a tiny bit of extra knowledge or experience can readily change a person’s view of a given moral dilemma to be the opposite of what it was prior to acquiring that knowledge (for instance, I myself switched from being a classical to a negative utilitarian based on a modest amount of information in the form of arguments I had not considered before). This high sensitivity to small changes in our brain implies that even a system with near-perfect information about some person’s present brain state would be forced to make a highly uncertain guess about what that person would actually prefer in a given moral dilemma. And the further ahead in time we go, and thus further away from our familiar circumstance and context, the greater the uncertainty will be.

By analogy, consider the task of AI alignment with respect to our ancestors ten million years ago. What would their preferences have been with respect to, say, the future of space colonization? One may object that this is underdetermined because our ancestors could not conceive of this possibility, yet the same applies to us and things we cannot presently conceive of, such as alien states of consciousness. Our current preferences say about as little about the (dis)normativity of such states as the preferences of our ancestors ten million years ago said about space colonization.

A more tangible analogy might be to consider the level of confidence with which we, based on knowledge of your current brain state, can determine your dinner preferences twenty years from now with respect to dishes made from ingredients not yet invented — a preference that will likely be influenced by contingent, environmental factors found between now and then. Not with great confidence, it seems safe to say. And this point pertains not only to dinner preferences but also to the most consequential of choices. Our present preferences cannot realistically determine, with any considerable precision, what we would deem ideal in as yet unknown, realistic future scenarios. Thus, by extension, there can be no such thing as value extrapolation or preservation in anything but the vaguest sense. No human mind has ever contained, or indeed ever could contain, a set of preferences that evaluatively orders more than but the tiniest sliver of (highly compressed versions of) real-world states and choices an agent in our world is likely to face in the future. To think otherwise amounts to a strange Platonization of human preferences. We just do not have enough information in our heads to possess such fine-grained values.

The truth is that our preferences are not some fixed entity that determine future actions uniquely; they simply could not be that. Rather, our preferences are themselves interactive and adjustive in nature, changing in response to new experiences and new information we encounter. Thus, to say that we can “idealize” our present preferences so as to obtain answers to all realistic future moral dilemmas is rather like calling the evolution of our ancestors’ DNA toward human DNA a “DNA idealization”. In both cases, we find no hidden Deep Essences waiting to be purified; no information that points uniquely toward one particular solution in the face of all realistic future “problems”. All we find are physical systems that evolve contingently based on the inputs they receive.*

The bottom line of all this is not that it makes no sense to devote resources toward ensuring the safety of future machines. We can still meaningfully and cooperatively seek to instill rules and mechanisms in our machines and institutions that seem optimal in expectation given our respective, coarse-grained values. The conclusion here is just that 1) the rules instantiated cannot be the result of a universally shared human will or anything close; the closest thing possible would be rules that embody some compromise between people with strongly disagreeing values. And 2) such an instantiation of coarse-grained rules in fact comprises the upper bound of what we can expect to accomplish in this regard. Indeed, this is all we can expect with respect to future influence in general: rough and imprecise influence and guidance with the limited information we can possess and transmit. The idea of a future machine that will do exactly what we would want, and whose design therefore constitutes a lever for precise future control, is a pipe dream.


* Note that this account of our preferences is not inconsistent with value or moral realism. By analogy, consider human preferences and truth-seeking: humans are able to discover many truths about the universe, yet most of these truths are not hidden in, nor extrapolated from, our DNA or our preferences. Indeed, in many cases, we only discover these truths by actively transcending rather than “extrapolating” our immediate preferences (for comfortable and intuitive beliefs, say). The same could apply to the realm of value and morality.

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