From Bacteria to Bach and Back

Part I: Turning Our World Upside Down

1. Introduction

• How come there are minds?
  • Minds evolved and created thinking tools that eventually enabled minds to know how minds evolved, and even to know how these tools enabled them to know what minds are.
  • What thinking tools? The simplest, on which all the others depend in various ways, are spoken words, followed by reading, writing, and arithmetic, followed by navigation and mapmaking, apprenticeship practices, and all the concrete devices for extracting and manipulating information that we have invented: compass, telescope, microscope, camera, computer, the Internet, and so on.
  • These, in turn, fill our lives with technology and science, permitting us to know many things not known by other species. We know there are bacteria. Even bacteria don't know there are bacteria.
  • Our minds are different. It takes thinking tools to understand what bacteria are, and we're the only species (so far) endowed with an elaborate kit of thinking tools.
• A birds-eye view of the journey:
  • Life has been around on Earth for around 4bn years. The first 2bn were spent optimizing the machinery for self-maintenance, energy acquisition and reproduction, and the only living things were relatively simple, single-celled entities - bacteria or their cousins, archaea: the prokaryotes.
  • Then an amazing thing happened. Two different prokaryotes collided and instead of one eating the other, it let it go on living, and, by dumb luck, found itself fitter, more competent in some way that mattered, than it had been before. This was perhaps the first successful instance of technology transfer. A fortuitous mutation almost never happens, but evolution depends on those rarest of rare events. This is the birth of the eukaryotes
  • Every living thing big enough to be visible to the naked eye is a multicellular eukaryote.
  • The Cambrian Explosion, which occurred over several million years about 530m ya, saw the sudden arrival of a bounty of new life forms.
  • The "MacCready Explosion", at the dawn of human agriculture, about 10k ya, transformed the terrestrial vertebrate biomass (excluding insects, other invertebrates, and marine animals). At the beginning, humans plus their livestock and pets make up only 0.1%, and now we make up 98% (mostly cattle). This explosion is based on three factors - population, technology, and intelligence (our so-called native intelligence depends on both our technology and our population numbers).
• Dennett identified the "romantic" and "killjoy" sides of the duel over the stature of animal minds. We are not the God-like geniuses we think we are, but animals are not so smart either, and yet both humans and other animals are admirably equipped to deal "brilliantly" with many of the challenges thrown at them.

2. Before Bacteria and Bach

• until there were systems that could be strictly called reproducing systems, the processes at work were only proto-evolutionary, semi-Darwinian, partial analogues of proper evolution by natural selection; they were processes that raised the likelihood that various combinations of ingredients would arise and persist, concentrating the feedstock molecules until this eventually led to the origin of life.
• A living thing must capture enough energy and materials, and fend off its own destruction long enough to construct a good enough replica of itself.
• The reverse-engineering perspective is ubiquitous in biology and is obligatory in investigations of the origin of life. It always involves some kind of optimality considerations: What is the simplest chemical structure that could possibly do x? Or would phenomenon x be stable enough to sustain process y?
• Orgel's second rule: "Evolution is cleverer than you are."
• Here is an example of a possible gambit in the origin of life:
  • It is tempting to assume that the very first living thing capable of reproducing must have been the simples possible living thing (given the existing conditions on the planet at the time).
  • Make the simples replicator you can imagine and then build on that foundation.
  • But this is by no means necessary. It is possible, and more likely, I think, that a rather inelegantly complicated, expensive, slow, Rub-Goldberg conglomeration of objets trouvés was the first real replicator, and after it got the replication ball rolling, this ungainly replicator was repeatedly simplified in competition with its kin.
  • Many of the most baffling magic tricks depend on the audience no imagining the ridiculously extravagant lengths magicians will go to in order to achieve a baffling effect. If you want to reverse-engineer magicians, you should always remind yourself that they have no shame, no abhorrence of bizarre expenditures for tiny effects that they can then exploit. Nature, similarly, has no shame - and no budget, and all the time in the world.
• Adaptionism is alive and well; reverse-engineering is still the royal road to discovery in biology.

3. On the Origin of Reasons

• There are three strategies to adopt when trying to understand, explain, and predict phenomena:
  • The physical stance - is the least risky but also the most difficult; you treat the phenomenon as obeying the laws of physics, and use physics to predict what will happen next.
  • The design stance - is only for things that have been designed, either artifacts or living things or their parts, and have functions or purposes.
  • The intentional stance - works primarily for things that are designed to use information to accomplish their functions. It works by treating the thing as a rational agent, attributing "beliefs" and "desires" and "rationality" to the thing, and predicting that it will act rationally.
• Evolution by natural selection is not itself a designed thing, an agent with purposes, but it acts as if it were. It is a set of processes that "find" and "track" reasons for things to be arranged one way rather than another.
• The reasons found by human designers are typically (but not always) represented in the minds of the designers, whereas the reasons uncovered by natural selection are represented for the first time by those human investigators who succeed in reverse-engineering Nature's productions.
• Our human world of reasons grew out of a world where there were no reasons.
• Two meanings of the word "why":
  • What for - "Why are you handing me your camera?"
  • How come - "Why does ice float?" This is asking for a cause or a process narrative
• Evolution by natural selection starts with "how come" and arrives at "what for". We start with a lifeless world in which there are no reasons, no purposes at all, but there are processes that happen.
• A central feature of human interaction, and one of the features unique to our species, is the activity of asking others to explain themselves, to justify their choices and actions, and then judging, endorsing, rebutting their answers, in recursive rounds of the "why?" game.
• Our capacity to respond appropriately in this reason-checking activity is the root of responsibility. Those who cannot explain themselves or cannot be moved by the reasons offered by others, those who are "dead to" the persuasions of advisors, are rightly judges to be of diminished responsibility and are treated differently by the law.
• The "logical space of reasons" is bound by norms, by mutual recognition of how things ought to go. Wherever there are reasons, there is room and need for some kind of justification and the possibility of correction when something goes wrong. This normativity is the foundation of ethics.
• But there are two kinds of norms and corrections:
  • social normativity - concerned with social norms, practice, and collaboration
  • instrumental normativity - concerned with quality control or efficiency, the norms of engineering
• Natural selection is an algorithmic process, a collection of sorting algorithms that are themselves composed of generate-and-test algorithms.
• In the prebiotic or abiotic world (before life), there were cycles at many spatio-temporal scales: seasons, night and day, tides, the water cycle, and thousands of chemical cycles discoverable at the atomic and molecular level, gradually changing the conditions in the world and thus raising the probability that something new will happen.
• This led to differential persistence, some temporary combinations of parts hang around longer than others. The rich can get richer, even though they can't yet bequeath their riches to descendants.
• Differential persistence must then somehow gradually turn into differential reproduction.
• "Serendipity" is when something good happens randomly, while "clobbering" is when something bad happens randomly.
• Walls or membranes that are randomly persisted are serendipitous in that they allow internal cycles to operate for a time without interference, and we see the engineering necessity of membranes to house the collection of chemical cycles - the Krebs cycle and thousands of others - that together permit life to emerge.
• Before we can have competent reproducers, we have to have competetent persisters. We are witnessing an automatic (algorithmic) paring away of the nonfunctional, crowded out by the functional.
• There are reasons why the parts are shaped and ordered as they are and this is the birth of reasons. Through Darwinism about Darwinism, we see the gradual emergence of the species of reasons out of the species of mere causes, what fors out of how comes*
• Natural selection is an automatic reason-finders, which "discovers" and "endorses" and "focuses" reasons over many generations.
• If there happens to be a "difference that happens to make a difference" then we have the germ of a reason, a proto-reason, and when this is selected to persist longer, then we can see emerge the accumulation of function by a process that blindly tracks reasons.
• Reasons existed before there were reasoners. There are reasons why trees spread their branches but they are not, in any strong sense, the trees' reasons. They don't "have" the reasons and they don't need to have the reasons.
• Darwin didn't extinguish teleology - he naturalized it.
• Reverse-engineering in biology is a descendant of reason-giving-judging.
• The evolution of what for from how come can be seen in the way we interpret the gradual emergence of living things via a cascade of prebiotic cycles. Free-floating rationales emerge as the reasons why some features exist; they do not presuppose intelligent designers, even though the designs that emerge are extraordinarily good.

4. The Strange Inversions of Meaning

• The world before Darwin was held together not by science but by tradition, through the trickle-down theory of creation from God, which Darwin replaced by the bubble-up theory of creation.
• Design space:
  • Skyhooks - float high in design space, unsupported by ancestors, the direct result of a special act of intelligent creation.
  • Cranes - are non-miraculous innovations in design space that enable ever more powerful lifting and efficient exploration of the space. Endosymbiosis is a crane, as are sex and language and culture.
• Turing showed that it was possible to design mindless machines that were absolutely ignorant, but that could do arithmetic perfectly, following "instructions" that could be mechanically implemented.
• This is "competence without comprehension" and Turing saw that it could provide a traversable path in design space from absolute ignorance to artificial intelligence.
• All the brilliance and comprehension in the world arises ultimately out of uncomprehending competences compounded over time into ever more competent - and hence comprehending - systems.
• This overthrows the pre-Darwinian mind-first vision of Creation with a mind-last vision.
• Darwin discovered evolution by natural selection, while Turing invented the computer, but he is one of the twigs on the Tree of Life who is, himself, an indirect product of the blind Darwinian processes.
• Distribution of expertise or understanding of this sort is a hallmark of human creative processes.
• "Ontology" is the set of "things" that an animal can recognize and behave appropriately with regard to, and equally the set of things that a computer program has to be able to deal with to do its job. Humans have extremely varied ontologies. Some believe in electrons and some believe in abominable snowmen, but there is a huge common core that is shared by all normal human beings from around 6 years old:
  • Manifest image - the things we use in our daily lives to anchor our interactions and conversations. For every noun in our everyday speech, there is a kind of thing it refers to. It comes along with your native language. It's the world according to us
  • Scientific image - populated with molecules, atoms, electrons, gravity, quarks. But even scientists spend most of their day in the manifest image.
• These two versions of the world that are now quite distinct were once merged or intertwined in a single ancestral world of "what everybody knows" that included all the local fauna and flora and weapons and tools and dwellings and social roles, but also goblins and gods and miasmas and spells.
• We can treat animals as having different ontologies without settling issues of whether they are conscious of them or simply the beneficiaries of designs that can be interpreted (by reverse engineers or forward engineers) as having those ontologies.
• A well designed elevator:
  • Has a kind of ontology. It is a good elevator if it interacts appropriately with its environment and its passengers. It uses variables to keep track of all the features of the world that matter to getting its job done and is oblivious to everything else.
  • It has no need to know what its ontology is or why - the rationale of the program is something only the program's designers have to understand.
  • Its prudent self-monitoring can be seen to be an elementary step towards consciousness.
• Even bacteria are good at staying alive, at making the right moves at the right times, but they have elevator-type minds, not elevated minds like ours. And these minds are the products of an R&D process of trial and error that gradually structured their internal machinery to move from state to state in a way highly likely - not guaranteed - to serve their limited but vital interests.
• But unlike the elevator there is noting at all that plays the rols of the labels or comments in a source program. There is nothing anywhere at any time in that R1D history that represents the rationales of it. But they can be discovered by reverse engineering - there is a reason why the parts are shaped as they are, why the behaviors are organized as they are, and that reason will "justify" the design (or an earlier design that has now become either vestigial or transformed by further evolution to serve some newer function.
• The elevator has replaced a human - the elevator operator - by a machine that "sorta" follows the same rules as the human. We humans often occupy this kind of intermediate level of consciousness where we have internalized or routinized through practice a set of explicit rules that we may then discard and even forget.
• The Manhattan project had a small number of intelligent designers who organized a massive group of people, most of whom knew nothing about what they were doing beyond their immediate tasks. The "need to know" principle means that it is possible to create very reliable levels of high competence with almost no comprehension for rather insulated tasks
• GOFAI can be seen in retrospect as an exercise in creating something rather Cartesian, a rationalistic expert with myriads of propositions stored in its memory, and all the understanding incorporated in its ability to draw conclusion. It relied on the comprehension of the designers to contrive systems composed of subsystems that were foresightedly equipped with exactly the competences they would need in order to handle the problems they might face.
• But modern deep-learning AI is bottom-up, using wasteful, mindless, less bureaucratic, more evolution-like processes of information extraction.
• Top-down intelligent designing works, but it is responsible for much less of the design in our world than is commonly appreciated.
• Comprehension, far from being a Godlike talent from which all design must flow, is an emergent effect of systems of uncomprehending competence; natural selection on one hand, and mindless computation on the other.