(This is a pre-editing version of the piece in Noēma that came out recently on this somewhat incendiary topic, along with a brief preamble.)
Are living things machines?
Yes.
also No.
Because nothing is fully captured by our formal models or their limitations.
There are no living things that aren’t, to some degree, amenable to sophisticated concepts of cybernetics, physics, and the sciences of computation and machine behavior. We all have machine-like aspects.
Here’s how orthopedic surgeons successfully use the machine metaphor: (images used by permission from Jan Cavel)
But then they send you home to heal, and the body does the rest – things we have no idea how to micromanage:
It’s very clear that our formal models, like Turing machine paradigms, are not sufficient to capture what is special about life.
But also, it turns out that there are no machines that aren’t, to some degree, also doing more than our simplistic stories of algorithms and materials lead us to expect. If we know how to look.
“Degree” is the operant part – models of transformation (not magical crisp categories) consistent with the latest findings in developmental biology and synthetic morphology.
And, given the diversity of machines, especially the recent explosion of work in artificial life (and machines that evolve and self-construct), the term “machine” – nowadays conveys almost nothing about the thing itself – only a little bit about your intent in how you plan to interact with it. And even then, nothing very informative has been said unless you specify what kind of machine you have in mind. You’re better off just being explicit about the set of tools (which discipline) you mean to apply.
“Life” is what we call those systems which are really good at aligning their parts toward an expanded cognitive light cone, projected into new problem spaces, and thus revealing creative, agential aspects (which, to some extent, are present all the way down). See here for a discussion of what our current machines and computer architectures are missing: creative interpretation of their own memories.
My claims here are simple:
(1) Nothing is any formal model; “machines” and “life” are perspectives – proposals about the kinds of tools one can use to interact with a system, not a statement about what it objectively is.
(2) The interoperability of life (components that originated by natural trial and error, merged with those that were engineered) enables a huge spectrum of diverse agents that cannot be parsed by binary “life” and “machine” categories. Especially because both kinds of components are the beneficiaries of ingressing patterns which can significantly potentiate agency.
And, most importantly and controversially:
(3) We correctly realize that our formal models of chemistry do not tell the whole story of mind; but we incorrectly seek to protect the majesty of life by an appeasement strategy in which we concede to the reductionist materialists that some things (so called “machines”) really are fully encompassed by our mechanical models and their limitations. I claim organicists should take their view more seriously: the same magic that infuses living things can, if one is willing to loosen our filters, be seen in the most minimal systems. It is everywhere, and does not obey the restrictions we try to place on it with artificial distinctions between life and machines.
An emergent field is thriving, developing tools for detecting and predicting the ubiquitous emergence of not just complexity and unpredictability, but of goal-directed competencies and problem-solving (intelligence). This has testable, practical consequences for regenerative medicine and bioengineering.
The keywords are humility, pluralism, observer-relative perspectives, and a commitment to experiment (fecundity of new research programs as a judge, vs. the gate-keeping of ancient philosophical categories).
What stands in the way is a remarkably successful story that almost everyone has bought into: that we know what materials can do. A recent example – from the movie Ex Machina (similar themes of course appear in many sci-fi stories). At one point the protagonist starts doubting the boundary between conventional minds and AI’s and cuts his arm open to make sure he’s not a robot himself. What’s interesting about that scene is this: the reason he’s doing it is that if he finds cogs and gears under his skin, he’s going to be upset – viewers understand that he will conclude “OMG I’m not real, and my mind is an illusion, I’m just a machine”. Now, why is that – why is the conclusion never this: “Well, I know I’m real, I’ve got 40 years of primary experience of my own agency and inner perspective, so if cogs and gears are inside of me, I guess I’ve just learned something interesting about cogs and gears! Looks like they can make real minds too. And frankly, while I took biochemistry and neurophysiology courses, they never did explain why the molecular cogs and gears I thought I was made of had any monopoly on making real minds (their trial-and-error origin, via evolution, doesn’t seem to prove these materials’ privilege). So – fine, cogs and gears of a different kind it is, moving on.”
But most people do not come to that conclusion, even if they haven’t had any biochemistry or neurophysiology courses or formulated a theory about the uniqueness of their squishy substrate. Why instead do they think they’ve learned a new fact about themselves, not about the cogs and gears. Why is the story of their own mind more amenable to change than their ingrained story about cogs vs. biochemicals? Because we’ve soaked up story in which we supposedly understand matter and what it can do, so well that we’re willing to doubt our own primary experience of our own reality and agency in favor of keeping up the commitment to wet chemicals as uniquely enabling mind. I find it remarkable that this “reality” becomes so ingrained that people will diss “machines” in every context, even if it means denying their own reality.
It’s the single best, most effective piece of propaganda I’ve seen – this physicalist worldview is pretty universal. The further amazing thing is that it’s not just the Western world. One might think, at least the Eastern and native traditions aren’t physicalists in that way. But they often are; I’ve had a number of experiences discussing these issues with Buddhists, Rabbis, and Indic scholars, and they have been generally very pessimistic about the possibility of artificial minds. They often seem committed to biochemistry as the only substrate that can do the trick – they are ok with spirits, but are very sure that spirits aren’t allowed to incarnate in robotic, intentional constructs, only in squishy, wet, accidentally-derived ones.
All in all, despite the fears of organicists who are threatened by extending the magic toward “machines”, the implications of this view are to see more life, not less. The goal is not to skew everything toward mechanism, but to find the optimal interaction protocols for diverse systems by reducing our mind-blindness and recognizing agency in unfamiliar guises and spaces.
Contrary to a number of recent opinion pieces, the machine metaphor hasn’t failed us – at least, it hasn’t failed those who never expected a single metaphor to do everything. And, it has failed us, but not just in biology and the sciences of the mind – in bioengineering too, because there are no machines the way some think – not among the biota, not anywhere. There is probably no dead matter anywhere, only minimally active matter and lazy observers.
Here’s a rough draft of the paper:
Living Things are not Machines (also, they Totally Are)
“All models are wrong, but some are useful”
— George E. P. Box
“There is nothing natural about classes, families and orders, the so-called systems are artificial conventions”
— Jean-Baptiste Lamarck
Never hire:
• an orthopedic surgeon who doesn’t think your body functions as a mechanical machine
• a psychotherapist who thinks it does
• an HVAC tech who doesn’t think thermostats have nano-goals
• a coder who thinks only physics, not “incorporeal algorithms”, makes electrons dance
• a bicycle-maker or synthetic biologist who delights in the novel, whimsical, and unpredictable agential quality found in their creations
• an AI engineer or synthetic morphologist who thinks that “we know what it can do because we built it and understand the pieces”
The question is, how do you want your cell biologist and regenerative medicine therapist to think?
Despite the continued expansion and mainstream prominence of molecular biology, and its reductionist machine metaphors [1] [2] [3,4], or likely because of it, there has been an increasing upsurge of papers and science social media posts arguing that “living things are not machines” (LTNM). There are thoughtful, informative, nuanced pieces exploring this direction, such as this one and others [5-13], masterfully reviewed and analyzed in [14]. But, many others use the siren song of biological exceptionalism, under-specified claims, and ungrounded terminology to push a view that misleads the lay reader and stalls progress in a number of fields. Evolution, cell biology, biomedicine, cognitive science (and basal cognition), computer science, bioengineering, philosophy – all of these are held back by the hidden assumptions in the LTNM lens that are better shed in favor of a more fundamental framework.
In arguing against LTNM, I should put my cards on the table. I use cognitive science-based approaches to understand and manipulate biological substrates [15]. I have claimed that cognition goes all the way down, publishing papers on memory and learning in small networks of mutually interacting chemicals [16,17] and on molecular circuits as agential materials [18]. I take the existence of goals, preferences, problem-solving skills, attention, memories, etc. in biological substrates such as cells and tissues so seriously that I’ve staked my entire laboratory career on this approach [19,20]. I routinely catch criticism from molecular biology colleagues who consider my views to be an extreme form of animism, in my claim that bottom-up molecular explanations simply won’t do [21,22]. My quarrel with LTNM is not coming from a place of sympathy with molecular reductionism; I consider myself squarely within the organicist tradition [23-31], even perhaps pushing further than many of its adherents would [21,32]. But LTNM has to go. Not to be replaced by Living things Are Machines, because that is equally wrong. Both hold back progress.
It is easy to see why LTNM persists. The LTNM framing gives the feeling that one has said something powerful – cut nature at its joints with respect to the most important thing there is, life and mind. It feels as if it forestalls the constant, pernicious efforts to reduce the majesty of life to predictable mechanisms, with no ability to drive moral worth or all of the 1st person experiences that make life worth living. But this is all smoke and mirrors, from an idea that took hold as an attempted bulwark against reductionism and mechanism and refuses to go away even though we have outgrown it. Here is the unfortunate package that comes with LTNM’s attractive coating:
- Many who support LTNM never specify whether they mean the boring 20th century machines, today’s quite different artifacts, or all possible future results of engineers’ efforts. Without answering the hard question of what a “machine” is – a point at the core of the LTNM’s claim – it offers nothing.
- It locks its adherents into unsolvable pseudoproblems as to the status of cyborgs, hybrots, and every possible kind of chimeric being that’s partly natural and partly engineered [33]. An increasing number of epicycles will be needed, as these beings come online, to accommodate the many special cases that don’t fit into LTNM’s binary classification.
- It signals that one supports the power of evolution, but fails to define its secret sauce, and explain why the gropings of random mutation and selection have a monopoly on making minds. Why can’t engineers use those same techniques and embody the amazing solutions found by the natural world in other media?
- It sounds grandiose – universal – but rarely do its proponents say what it means for life broadly, in the universe. Would they assess functional capabilities, composition, or origin-story as evidence when evaluating the moral standing of an eloquent and personable alien visitor who is kind of shiny and metallic-looking, but doesn’t know if she evolved or came into the world with the help of other minds?
It’s disingenuous to say that the mechanistic approach to life has not contributed in major ways to knowledge and capabilities – of course it has, from orthopedic surgery to vaccines and much more. On the other hand, many knowledge gaps and functional outcomes remain un-addressed; it’s likely that the mechanistic approach has already picked much of the low-hanging fruit in many aspects of science and now must be augmented by top-down approaches [34]. So, what are we to make of claims that life can be understood using the machine metaphor? There is currently little beneficial cross-talk between the organicist and mechanist camps, who differ so strongly in their claims of what life is.
“Whatever you say it is, it isn’t.” — Alfred Korzybski
My proposed solution is to lean into the realization that nothing is anything, and drop the literalism that mistakes our maps for the territory. Let’s stop confusing our formal models (and their limitations) with the thing we are trying to understand, and pretending that there is a single, universal, objective metaphor that is really true of “living things” while the others are false. In other words, let’s reject the one thing that the organicists and mechanists agree on: that there is one correct, real picture of systems and we just need to discover which one is right.
I propose instead that it’s all about perspective and context. In some scenarios, certain formalisms and tools appropriate for some kinds of machines will pay off; in other scenarios, they are woefully inadequate. If we give up the primitive idea that there needs to be one correct answer, and get comfortable with having to specify context and payoff, we can make real progress. On the one hand, this pluralistic idea is simple, unsurprising, and ancient. On the other hand, failure to absorb this lesson is at the root of many of today’s disagreements and brakes on progress.
All terms – cognitive ones, computationalist ones, and mechanistic ones – are not really claims about what the system is; they are statements of a proposed protocol that one has picked with which to relate to a system. They range across toolkits such as rewiring, cybernetic steering, training, teaching, and love (and many more). Each has its own discipline, assumptions, tools that provide powerful leverage, and blind-spots. It’s a wide spectrum and multiple approaches will pay off in diverse ways (or not, but that’s the empirical game we’ve taken on as scientists). Many can be true at once.
(image used with permission, by Jeremy Guay of Peregrine Creative)
The “machines or not” (or “intelligent or not”, or “purposeful or not”, etc.) framing is a sure path to unresolvable pseudoproblems if we take it in the sense of binary, objective categories describing natural kinds. I propose an engineering (writ large) approach: what we are really saying when we make those claims is: “here is the bag of tools – e.g., rewiring, cybernetics, behavior-shaping, or psychoanalysis – that I propose to use to relate to this system. Let’s all see how well that turns out for me”. Then, we can see that all of these terms indicate rich continua, not binary categories, and that multiple observers’ viewpoints can be effective (insightful, powerful), in their context, because no one is exclusively right. An orthopedic surgeon should see your body as a simple mechanical machine – they’ve got hammers and chisels and it works very well. A psychotherapist should not see you as a simple mechanical machine. What should a worker in regenerative medicine see in your cells? Or an evolutionary developmental biologist? That is an empirical question, to be settled by trying the various tools and seeing how far one can get. But what we do know is that “machine” now covers an incredible variety of tools and approaches (including ones that make use of evolutionary dynamics, cybernetic goal-directedness, self-construction and self-reference, open-ended reasoning, lack of separation of data from hardware thus breaking the Turing paradigm, etc.) – we have left the age where “machines” were easy to delineate because we were so limited in our understanding of the tools required to understand and make machines (it turns out, some of the same tools behavioral scientists and biologists have been using for a long time).
Further, I think the magic that makes the old machine metaphors too limited for living systems applies likewise to even minimal systems we intuitively think should well-described by our formal models. I propose that the better path forward is based on pluralism and pragmatism, and a humility about not confusing our formal models (and their limitations) for things themselves, living or not, and being as open to surprising emergence of proto-cognition in unconventional places as we are to its emergence in natural biology, because we still don’t know enough to assume we know where it can and cannot be found.
(image used with permission, by Jeremy Guay of Peregrine Creative)
The days of being loose with colloquial terminology, and of pretending we have binary, easy-to-recognize categories that neatly split between machines and living beings, are over. They’re not coming back, given the advances in bioengineering and active matter research and the obvious realization that evolution is not magical creation, and that inside our cells is the same kind of matter that engineers can manipulate, not fairy dust. That’s good because those terms were never good to begin with – they sufficed, barely, in prior ages due to limitations of technology and imagination. Using “machine” to call up people’s visions of boring, deterministic, “we know what it does” objects of the past simply masks our ignorance and holds back progress on the most fascinating open problems of the century.
Let’s also abandon the view that there are “just metaphoric ways of speaking” and then there are real scientific explanations. Everything is a metaphor – all we have are metaphors, some of which are better than others at helping us get to the next, more empirically interesting and generative metaphor. There are few to none inherently bad metaphors that we can detect from a philosophical armchair as errors that run afoul of some dusty old category; all we have are metaphors that facilitate (or hold back) discovery to various degrees, and categories that flexibly change with the science. And the science is clear – we now have a non-magical ways to understand goals, downward causation, self-reference, plasticity, and much more [35-44]. The reductionist/mechanist camp will have to adjust to the fact that cognitive tools, applied to things that aren’t brainy animals, are not “just metaphors” – they, like “pathways”, are legitimate hypotheses that will live or die by their consequences at the bench. The organicist camp will have to live with the fact that computational perspectives are also just metaphors, not essentialist denigrations of life’s majesty.
Let’s get on with the good science of being very specific about our metaphors and what they facilitate vs. constrain. Let’s specify, every time, precisely where on the spectrum one plans to approach a system, and be clear that this is a claim about that particular research effort, not a claim about a thing, and that we are all in the business of generating and testing metaphors.
Everything I’ve said above should not be shocking. It has massive implications, which many don’t like, but it rests on well-trodden philosophical positions which aren’t particularly outrageous. And the bottom line, and perhaps my most controversial claim, is this: what hampers progress now is a lack of humility. The feeling on both sides that we understand what materials can do, and what algorithms can do. The idea that because you’ve made something, and know its parts, that you understand its capabilities and its limitations. We do not – we’re just scratching the surface. It is remarkable that in denying the precious magic (agency, cognition, etc.) to “machines”, the organicists have bought in to the reductionists’ most audacious claim: that when you know the properties of the parts, you know its true nature.
In an influential piece [45], David Chalmers framed the ‘hard problem’ of consciousness as: “Why should physical programming give rise to a rich inner life at all? It seems objectively unreasonable that it should, and yet it does.” This same assumption pervades numerous fields: that we have enough knowledge, and the right cognitive system, to have a well-calibrated intuition about what is reasonable and what kind of systems have (proto)cognitive properties. I think we do not, and thus caution and an open mind are our best guides.
There are many reasons to reject naïve computer and machine frameworks in the study of life and mind. Of course living things aren’t fully encompassed by computationalist or simple machine metaphors. But neither are “machines”. Physical systems are NOT “machines” any more than living things are, because machines are our formalizations and inevitably miss key aspects of reality. Not even simple algorithms are fully encompassed by our picture of what the algorithm is doing [46]. We need to come to grips with the fact that all our frames will miss important aspects of things, that it’s ok to say something about a system without claiming you’ve said everything, and that even the simplest of systems can exert surprising effects that reach higher on the Wiener-Rosenbleuth scale [42] than simple emergence of complexity or stupid unpredictability. Synthetic systems, which we might think are following an algorithm, may or may not have a degree of true mind, but it won’t be because of the algorithm they are following (any more than our mind is real because of the laws of chemistry being followed). Emergence of cognition, in a strong way that is facilitated but not circumscribed by the embodiment on which it supervenes, is the research frontier for the next century, and it applies equally well to designed, evolved, and hybrid systems. If, as Magritte pointed out, not even a pipe is encompassed by the limitations of our representation of it, how much less so are dynamic creations, living and otherwise.
I call upon the organicist community to pursue their approach fearlessly: the reason that living things are not entirely described by mechanist metaphors is the exact same reason that “machines” are not entirely described by them either. Organicism gives us a great tool – respect for the surprising emergence of higher-order aspects of cognition; take this tool seriously and apply it fearlessly. Minds, and the respect they are due, are not a zero-sum game. It’s alright to see “machines” as somewhere on the same spectrum as us – we won’t run out of compassion (a common driver of the scarcity mindset with respect to attributing cognition) if we extend the possibility of emergent minds beyond its most obvious proteinaceous examples.
This view isn’t popular with either side; stark categories and crisp distinctions between viewpoints are more comfortable than continua – they make everything simpler. But when pushed as above, some people will back off from LTNM to the claim that “fine, it’s today’s machines that are nothing like life.” And with this I largely agree, though those kind of claims, like political bumper stickers, have a very short shelf-life. Unfortunately, “Life is not Like Today’s Machines” is not as catchy and magnetic a title, so no one leads with this, more defensible, view. People outside the field read the more grandiose claim and assume we have good theory behind it, while everyone in the field knows the limitations but often won’t make them explicit in their writing.
To summarize the approach I advocate, anchored by the principles of pluralism and pragmatism: nothing is anything, but if we move beyond expecting everything to be a nail for one particular favorite hammer, we are freed up to do the important work of actually characterizing sets of tools that may open new frontiers. We owe stories of scaling and gradual metamorphosis along a continuum, not of magical and sharp “great transitions”, and a description of the tools we propose to use to interact with a wide range of systems, along with a commitment to empirical evaluation of those tools. We must battle our innate mind-blindness with new theory in the field of diverse intelligence and the facilitating technology it enables, much as a theory and apparatus of electromagnetism enabled access to an enormous, unifying spectrum of phenomena of which we had previously had only narrow, disparate-seeming glimpses. We must resist the urge to see the limits of reality in the limits of our formal models [47]. Everything, even things that look simple to us, are a lot more than we think they are, because we too are finite observers – wonderous machines with limited perspective but massive potential and the moral responsibility to get this (at least somewhat) right.
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Featured image by Jeremy Guay of Peregrine Creative.
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